"Discriminatory Algorithms and Cultural Complexities" | CAS Inquire

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02/27/2020 Added

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Drs. Carrie Heitman and Heather Richards-Rissetto of the Department of Anthropology give this CAS Inquire talk. cas.unl.edu/cas-inquire

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[00:00:03.650]I have the outfit (mumbles)
[00:00:05.520]ready to go for the talk. (laughing)
[00:00:07.470]So I'm Heather Richards Rissetto
[00:00:09.650]and soon I'll be joined by Carrie Heitman in a bit
[00:00:14.530]but imma start out the talk
[00:00:17.810]so before we begin, we wanna thank the CAS Inquire committee
[00:00:21.740]and June Griffin for bringing to (mumbles) this program
[00:00:25.460]and for inviting us to speak today.
[00:00:27.600]We'd also like to thank our anthro colleague Taylor
[00:00:30.590]for directing CAS Inquire
[00:00:31.950]and congratulate him on his great success
[00:00:33.880]in its inaugural year.
[00:00:37.992]While computers now influence some might say infiltrate
[00:00:41.380]nearly every aspect of our lives,
[00:00:43.660]they were originally designed for number crunching
[00:00:46.460]that is they were designed to perform basic calculations
[00:00:49.240]on big data sets, much faster than humans could.
[00:00:53.140]Today, computers are integral to education, healthcare,
[00:00:57.000]entertainment, transportation, agriculture, our social lives
[00:01:02.000]well, basically everything.
[00:01:04.310]While we may be aware of their presence,
[00:01:06.260]we might often not notice their influence
[00:01:09.310]on our daily decision making.
[00:01:11.890]Everyday computers are making decisions for us, or are they?
[00:01:15.870]Are computers really taking the place
[00:01:17.430]of human decision making?
[00:01:19.753]Yes and no.
[00:01:21.730]Our objective in this talk is to delve deeper
[00:01:24.060]into this question, through exploration of algorithms
[00:01:27.600]and their influence on culture.
[00:01:32.010]But before we delve into how algorithms affect our lives,
[00:01:34.800]whether positively or negatively, and then reflecting
[00:01:37.720]on the future, let's begin with the basics.
[00:01:40.710]What are algorithms?
[00:01:42.130]And how do they relate to artificial intelligence
[00:01:44.300]and machine learning two terms currently inundating media?
[00:01:51.720]What exactly is an algorithm?
[00:01:54.200]We hear this word almost every day
[00:01:56.690]and yet to most of us, algorithms are still daunting
[00:01:59.120]and mysterious but algorithms are simply a set of guidelines
[00:02:02.970]that describe how to perform a task.
[00:02:05.670]Remember in elementary school when you had to stand
[00:02:07.970]up in front of the class and give a speech
[00:02:10.310]on how to perform a task such as baking a ranza?
[00:02:13.590]Or how to navigate traffic on a (mumbles) game day?
[00:02:17.220]Well, essentially you created an algorithm.
[00:02:20.050]A sequence of instructions on how to do something.
[00:02:23.230]In our digital age however, algorithms typically refer
[00:02:26.250]to a sequence of instructions telling a computer
[00:02:28.700]what to do rather than a human.
[00:02:31.340]Basically, an algorithm is just computer code
[00:02:34.140]and the software is fundamentally composed of algorithms.
[00:02:38.420]So, not that mysterious after all.
[00:02:43.060]So, how do algorithms impact our daily lives?
[00:02:46.470]Algorithmic operations are behind all of the responses
[00:02:51.040]that we get from digital assistance such as Siri, Cortana,
[00:02:54.510]or Alexa, when we ask them a question,
[00:02:57.310]they use one set of algorithms to interpret
[00:02:59.820]or get a sense of what you've asked,
[00:03:02.110]and then another set to acquire the information
[00:03:04.520]you've requested so, just this morning I asked Siri,
[00:03:08.970]what should I have for breakfast?
[00:03:10.670]She responded broccoli
[00:03:12.520]So I think the algorithm might be a little bit (mumbles)
[00:03:15.352]but we'll see unless you love broccoli so.
[00:03:18.620]Social media is wrought with algorithms,
[00:03:21.030]Instagram, Twitter, Facebook,
[00:03:23.600]and these algorithms are constantly changing.
[00:03:26.250]Prior to late 2019, Facebook's newsfeed algorithm ranked
[00:03:30.540]and displayed posts for users based on relevance
[00:03:33.980]driven primarily by businesses, brands and media.
[00:03:37.630]Now Facebook is using a new algorithm to select news feed
[00:03:40.870]based on the likelihood that a user will have a positive
[00:03:43.730]reaction, they do this by studying your friends,
[00:03:47.220]family and groups so that's not creepy at all.
[00:03:54.129]And oops, back here (mumbles)
[00:03:57.477]And recently, McDonald's bought an algorithm
[00:04:00.410]for data driven meal choices that can automatically change
[00:04:03.670]menus depending on the weather, time of day
[00:04:06.340]and traffic and of course suggest an order
[00:04:08.680]based on your license plate.
[00:04:12.640]So how does such algorithms relate
[00:04:14.760]to artificial intelligence and machine learning?
[00:04:18.320]Artificial intelligence or ai is an umbrella term,
[00:04:21.550]referring to instances where machines simulate human
[00:04:24.120]behavior using human intelligence.
[00:04:27.270]When a machine completes tasks based on a set
[00:04:29.080]of instructions, that is an algorithm,
[00:04:31.510]this intelligent behavior is what we call artificial
[00:04:33.880]intelligence, machine learning is simply a type of ai.
[00:04:38.840]In the case of algorithms, instead of repeatedly processing
[00:04:41.730]a stable set of instructions, systems based on machine
[00:04:45.070]learning, rewrite themselves as they work.
[00:04:47.690]In other words, humans write the original algorithm
[00:04:50.450]or computer code, but then the computer modifies
[00:04:53.120]this original algorithm based on what it's told to learn,
[00:04:56.330]what it's learning from that is its training set
[00:04:59.170]and how it's learning.
[00:05:03.280]So this brings up two critical points.
[00:05:05.960]First, we must remember is Cathy O'Neil,
[00:05:08.680]author of " Weapons of Math Destruction" writes,
[00:05:11.760]algorithms are opinions embedded in code.
[00:05:14.870]Humans write the rules that comprise algorithms,
[00:05:17.930]and who typically writes these rules?
[00:05:19.830]Well those, typically with wealth and power.
[00:05:28.110]Second, in a similar vein, Meredith Whittaker,
[00:05:31.560]a former Google employee was quoted saying,
[00:05:34.113]" What's frightening about ai isn't terminators
[00:05:36.560]and super intelligent machines, it's the way ai works
[00:05:40.140]to centralize knowledge and power in the hands
[00:05:42.320]of those who already have it, and further disempower
[00:05:44.980]those who don't."
[00:05:46.660]But before we go down that road,
[00:05:48.160]let's talk about some potential positives of algorithms.
[00:05:53.020]For example, self driving vehicles or buses
[00:05:56.340]can potentially lead to safer roads or fewer traffic jams
[00:05:59.200]reducing carbon emissions.
[00:06:01.120]So here you're seeing Chancellor Green,
[00:06:03.440]avoiding a self driving bus in Lincoln's 2018
[00:06:06.680]autonomous shuttle project.
[00:06:09.010]Another potential positive is in retail,
[00:06:11.370]using real time geo fencing,
[00:06:13.950]which location based algorithms trigger notifications
[00:06:18.910]on sales, best prices and even offer coupons
[00:06:22.700]as you walk down the aisles of your favorite store.
[00:06:26.122](chuckling)
[00:06:27.530]That makes me wonder what Big Brother was,
[00:06:29.007]but in this week, (chuckling)
[00:06:31.300]so text and speech recognition algorithms can recognize
[00:06:36.290]and translate written text into spoken language
[00:06:39.240]and vice versa, and vice versa translate spoken language
[00:06:42.950]into text or even into drawings.
[00:06:49.137]There is a moon at night,
[00:06:53.780]two trees in the field.
[00:07:00.543]Some flowers.
[00:07:07.428]Far away there's a house.
[00:07:09.401](chattering)
[00:07:12.826](mumbles)
[00:07:14.530]So this Google experiment turns real time free speech
[00:07:18.020]into animated drawing, such tools can possibly facilitate
[00:07:21.660]web accessibility for disabled persons,
[00:07:23.960]foster alternative modes of communication and much more.
[00:07:31.240]With global population growth and aging populations,
[00:07:34.100]healthcare is one of the world's fastest growing
[00:07:36.150]and most important industries
[00:07:38.150]and algorithms are changing medical practice from diagnosing
[00:07:41.110]and even predicting disease to discovering new medications.
[00:07:45.320]Google software can look into our eyes to assess our risk
[00:07:48.130]for a heart attack or stroke, other algorithms can spot DNA
[00:07:53.050]mutations and tumors and artificial intelligence
[00:07:56.400]is being used to improve breast cancer screening
[00:07:59.890]or predict lung cancer, which is the deadliest cancer
[00:08:03.150]in the world and the sixth most common cause of death.
[00:08:06.710]Recently, an algorithm discovered a much needed,
[00:08:10.000]entirely new antibiotic called Palicin
[00:08:13.050]which successfully destroys several bacteria straints
[00:08:16.400]including some of the most dangerous drug resistant bacteria
[00:08:19.160]on the World Health Organization's most wanted list.
[00:08:26.210]In 2018, Google's art and culture released an app
[00:08:30.560]enabling you to find your art doppelganger,
[00:08:34.450]what could be better?
[00:08:35.790]You took a selfie and Google's facial recognition algorithm
[00:08:38.690]searched the database of art from 17 museums worldwide
[00:08:41.960]to find your twin.
[00:08:47.275](upbeat music) Do I look like her?
[00:08:49.813](upbeat music)
[00:08:54.720]I feel very fancy looking at these, like all of them have
[00:08:58.080]a Victorian collar, their hair is curled,
[00:09:00.720]they have a lot of jewelry (chattering)
[00:09:02.954]and the painting styles are really different
[00:09:04.371]too, like I can tell they're from different eras.
[00:09:07.798](upbeat music)
[00:09:11.454]I think the beard might be a little misleading
[00:09:14.090]because all the results have beards so far,
[00:09:17.420]so I think it's pretty much focusing on that aspect
[00:09:19.680]as opposed to other things.
[00:09:21.389](upbeat music)
[00:09:24.123]I would say it picked up well on the nose.
[00:09:33.982]So, will your selfie be in a museum one day?
[00:09:39.155]Nop.
[00:09:41.477]While finding your museum doppelganger is fun,
[00:09:44.680]the science behind it is scary and Arwa Mahdawi,
[00:09:48.860]a columnist for The Guardian writes
[00:09:51.010]in reference to the doppelganger project, I quote,
[00:09:54.173]" Google's latest experiments is less about art
[00:09:56.870]than it does about the burgeoning science
[00:09:58.490]of facial recognition technology
[00:10:00.117]and the rise of the surveillance state."
[00:10:05.720]So as great as it might sound to have your face
[00:10:08.360]or your selfie in a museum one day as archeologists,
[00:10:11.090]Carrie and I can't help to ponder this excerpt
[00:10:13.450]from the New Yorker by Adrian channel.
[00:10:15.833]" What does it mean that our cultural history,
[00:10:18.170]like everything else is increasingly under the watchful eye
[00:10:20.860]of a giant corporation whose business model
[00:10:23.420]rests on data mining?
[00:10:25.350]One dystopian possibility offered by critics
[00:10:28.130]in the wake of the Google selfie app,
[00:10:30.020]was that Google was using all of the millions
[00:10:31.840]of unflattering photos to train its algorithms."
[00:10:35.260]Those that you saw in the video of people using.
[00:10:38.123]" Google has denied this but training goes both ways,
[00:10:41.550]as Google scans and processes more of the world's cultural
[00:10:45.070]artifacts, it will be easier than ever to find
[00:10:47.880]ourselves in history, so long as we offer
[00:10:50.360]ourselves up to the computers gaze."
[00:10:52.840]So on that sobering note,
[00:10:54.180]I'm gonna turn it over to Carrie.
[00:11:04.462]Hi everybody?
[00:11:05.295]It's a pleasure to be here.
[00:11:09.230]So there's no doubt that artificial intelligence
[00:11:11.450]or ai is seeping into nearly every aspect of our lives
[00:11:14.410]whether we know it or not.
[00:11:16.450]According to UC Berkeley computer scientist, Dr. Stuart
[00:11:19.040]Russell, the upside of such technologies is enormous
[00:11:22.960]a potential golden age for humanity
[00:11:25.840]but as informed citizens, we also need to understand
[00:11:28.480]the associated risks, the application of complex algorithms
[00:11:31.810]is continuing to expand in ways that many find alarming
[00:11:35.120]or at the very least, outstripping our ability to assess
[00:11:38.190]the associated risks and while this is not an exhaustive
[00:11:41.470]list of such potential negative consequences,
[00:11:44.460]we'd like to share with you a few potential examples.
[00:11:51.011]Let's see if this will work.
[00:11:53.984](mumbles) okay
[00:11:55.420]So fortune 500 companies such as Hilton,
[00:11:57.840]Goldman Sachs and Unilever are using ai
[00:12:00.540]in their assessments of job candidates.
[00:12:02.880]They work with third party vendors like HireVue
[00:12:05.890]to use a candidate's computer or cell phone
[00:12:08.470]camera to record job interviews,
[00:12:10.620]their proprietary algorithm,
[00:12:12.420]then analyzes the person's facial movements, word choice,
[00:12:16.510]speaking voice before ranking them against other applicants
[00:12:20.760]based on an automatically generated employability score.
[00:12:24.360]This is called affect recognition
[00:12:26.620]and HireVue claims that they can measure qualities
[00:12:28.910]such as grit and track how many times a candidate smiles
[00:12:33.070]and if you are interested, you should just go on YouTube,
[00:12:36.600]and there's just like oodles of like videos
[00:12:38.410]about how to trick the system to try to game
[00:12:41.110]yourself in terms of your employability score.
[00:12:44.130]Companies like HireVue and VCV claim that ai
[00:12:47.130]removes human biases that are rampant in hiring practices
[00:12:50.750]in their 2019 annual report
[00:12:52.730]however, the ai Now Institute points
[00:12:55.790]out that affect recognition
[00:12:58.210]has not been proven to be scientifically valid
[00:13:00.740]and that such technologies may simply automate
[00:13:02.810]discrimination, Whittaker, who heard from earlier
[00:13:06.320]of ai Now states, quote,
[00:13:07.563]" It's profoundly disturbing,
[00:13:09.740]that we have proprietary technology that claims
[00:13:12.410]to differentiate between a productive worker
[00:13:14.800]and a worker who isn't fit based on their facial movements,
[00:13:18.730]their tone of voice and their mannerisms."
[00:13:23.770]A 2019 study published in the Journal of Science,
[00:13:27.220]found evidence of racial bias in one algorithm widely used
[00:13:30.760]within the US healthcare system.
[00:13:32.750]According to the researchers,
[00:13:34.030]the bias occurred because the algorithm uses health costs
[00:13:38.130]as a proxy for health needs.
[00:13:40.580]The author's estimated that this racial bias
[00:13:42.560]reduces the number of black patients
[00:13:44.670]identified for extra care by more than half,
[00:13:47.840]less money is spent on black patients
[00:13:49.700]who have the same level of need
[00:13:51.420]and the algorithm does falsely concludes
[00:13:53.570]that black patients are healthier
[00:13:55.130]than equally sick white patients.
[00:13:57.500]This is a case where existing structural inequalities
[00:13:59.970]based on race, were already part of the data input
[00:14:03.050]and thus were exacerbated
[00:14:04.450]by the Optum proprietary algorithm.
[00:14:07.360]The good news is that the researchers found
[00:14:09.840]that in the racial bias in predicting who needs care,
[00:14:12.550]this could be eliminated by reformulating the algorithm
[00:14:15.490]so that it no longer uses cost as a proxy for needs.
[00:14:20.050]In addition to algorithmic biases in healthcare,
[00:14:22.620]they've also been a number of high profile cases
[00:14:24.610]of racial discrimination in facial recognition algorithms.
[00:14:27.920]One with Google's facial recognition algorithm
[00:14:30.830]that misidentified black faces and another were Nikon's
[00:14:34.120]Coolpix digital camera exhibit bias against Asian faces
[00:14:37.480]with regard to its " Did Someone Blink Morning?"
[00:14:44.630]With this case, in terms of criminal prediction
[00:14:48.000]and sentencing, we see just one of the troubling examples
[00:14:51.130]of algorithmic governance,
[00:14:53.370]where algorithms are now being used to govern citizens.
[00:14:56.410]In 2016 Propublica published an investigative expose
[00:15:00.570]of how criminal justice officials are using risk prediction
[00:15:03.930]equations to inform their decisions about bail,
[00:15:06.950]sentencing and early release.
[00:15:09.090]They collected the risk assessment scores
[00:15:11.240]for more than 10,000 people arrested for crimes
[00:15:14.020]in Florida's Broward County and check to see
[00:15:16.610]how many were charged with further crimes within two years.
[00:15:20.310]They were looking to see if those with higher risk
[00:15:22.490]assessments went on to be arrested for new crimes
[00:15:25.530]compared to those with lower risk assessments
[00:15:28.370]what they found was a racial disparity.
[00:15:31.060]The data showed that black defendants were twice
[00:15:34.200]as likely to be incorrectly labeled as higher risk
[00:15:37.650]than white defendants, conversely, white defendants
[00:15:40.610]labeled low risk were far more likely to end
[00:15:43.220]up being charged offenses than African Americans
[00:15:46.560]with comparably low compass risk score.
[00:15:49.650]You can see one example highlighted on the screen
[00:15:51.610]where the white defendant was given a low risk score
[00:15:53.780]of three but went on to commit three subsequent offenses
[00:15:57.270]within the two year study,
[00:15:58.830]whereas an African American didn't was given a higher risk
[00:16:01.370]score of 10 but went on to commit no subsequent offenses.
[00:16:05.170]Within their 10,000 person study,
[00:16:07.540]the results showed that the risk assessment predictions
[00:16:10.210]overestimated risk for African American defendants
[00:16:14.600]and underestimated risk for white defendants.
[00:16:17.340]The good news here is that the in the aftermath
[00:16:19.610]of this investigative journalism piece,
[00:16:21.530]there's been a concerted effort to help improve
[00:16:23.810]the compass algorithm to reduce racial bias
[00:16:26.270]in risk assessment.
[00:16:29.720]Warning, things are gonna get even more grim (chuckling)
[00:16:33.310]According to some ai experts,
[00:16:35.360]the most serious short term risk
[00:16:37.370]involves autonomous weapons.
[00:16:39.620]The company, STN, for instance,
[00:16:42.350]is working with the Turkish Government
[00:16:43.980]to create swarm drones that use facial recognition
[00:16:47.100]technology to kill a target.
[00:16:51.898](upbeat music)
[00:17:41.458]Given what we already know about the risks and weaknesses,
[00:17:44.020]of facial recognition algorithms,
[00:17:45.840]this military applications is particularly chilling.
[00:17:50.360]In response to this growing risk a coalition of ai
[00:17:53.480]researchers and advocacy organizations commission to 2017
[00:17:57.310]film called " Slaughterbots."
[00:17:59.450]The film asks the question," What happens
[00:18:02.050]when miniature drones with ai facial recognition
[00:18:05.960]and three grams of explosive programmable to kill
[00:18:08.860]a specific individual become available."
[00:18:11.890]Spoiler alert, it's not good.
[00:18:14.710]The film was presented to the United Nations Convention
[00:18:17.290]on conventional weapons, hosted by the campaign
[00:18:19.870]to stop killer robots.
[00:18:24.770]On a more academic note, a research on ai
[00:18:27.300]and their underlying algorithms has created
[00:18:29.180]by what some account as a crisis in science
[00:18:32.760]a foundation of scientific inquiry is the expectation
[00:18:35.470]that results can be replicated, verified,
[00:18:38.220]unpublished codes and sensitivity to training conditions
[00:18:41.280]have made it difficult for ai researchers
[00:18:43.600]to produce many key results.
[00:18:45.930]We can add to this broad concern the issue of large scale
[00:18:48.450]data sets used in training, which are largely in the hands
[00:18:51.340]of just a few for profit corporations like Amazon,
[00:18:54.180]Google, Facebook, IBM, and Microsoft.
[00:19:00.100]Those of you paying close attention may have noticed
[00:19:01.840]a recurring pattern in the negative consequences
[00:19:04.530]we've mentioned thus far.
[00:19:06.230]These issues were brought forward not by the industries,
[00:19:09.010]or the corporations using the algorithms in question
[00:19:12.070]but by outside researchers, journalists,
[00:19:14.600]and concerned citizens.
[00:19:16.240]So looking ahead, we might reasonably ask the question,
[00:19:18.843]" Do we trust corporations who are using these technologies
[00:19:22.250]for profit and competitive advantage to police themselves?"
[00:19:26.760]There are various other areas of concern
[00:19:28.430]that we'll briefly just mention
[00:19:29.610]here, and for those of you who are interested,
[00:19:32.170]we'd urge you to look at the 2019 report published
[00:19:35.030]by the ai Now Institute.
[00:19:37.840]According to their latest report,
[00:19:39.180]algorithms are increasing the power asymmetry
[00:19:41.570]between workers and employees.
[00:19:45.660]Employers sorry through applications
[00:19:47.820]such as algorithmic wage control,
[00:19:50.120]governments are increasingly adopting ai systems to surveil
[00:19:53.780]and control people but without regulation,
[00:19:56.380]and corporations are expanding the scale
[00:19:58.330]and scope of algorithmic health infrastructures.
[00:20:03.690]These technologies are disproportionately impacting
[00:20:06.280]vulnerable communities, people of color, women and the poor.
[00:20:10.810]It is also important to note that these ai systems
[00:20:13.360]are vulnerable to bad actors who can utilize backdoor access
[00:20:17.160]to retrain the system on bad data.
[00:20:19.700]An algorithm optimized for click through for instance,
[00:20:22.730]can be used to manipulate public opinion.
[00:20:26.030]At a broader level, what are the issues at play
[00:20:28.390]that we should think about in the deployment
[00:20:29.920]of these technologies across such diverse domains?
[00:20:35.315]Campolo and Crawford have argued that there's a discourse
[00:20:37.870]of enchanted determinism,
[00:20:39.760]which surrounds artificial intelligence.
[00:20:42.450]Ai systems are portrayed as exceeding human intelligence
[00:20:45.300]or performance a form of magical or alien genius.
[00:20:49.420]This rhetoric they argue, serves to shield the creators
[00:20:52.599]of these systems from the consequences
[00:20:55.640]their designers may not fully anticipate,
[00:20:58.000]understand or control.
[00:21:02.040]This leads us to an accountability gap,
[00:21:04.130]where deep learning systems might be considered
[00:21:06.300]beyond typical ethical responsibilities
[00:21:09.090]and legal liabilities.
[00:21:10.790]In 2019, Crawford and Shultz published an article
[00:21:14.290]in the Columbia Law Review titled
[00:21:16.233]" Ai systems as state actors."
[00:21:18.880]In the article, they observe how governments
[00:21:20.850]are increasingly turning to third party vendors
[00:21:23.950]to provide algorithmic architecture for public services,
[00:21:27.620]including welfare benefits and criminal risk assessments
[00:21:31.120]but when challenged, many state governments
[00:21:33.410]have disclaimed any knowledge or ability to understand,
[00:21:36.500]explain, or remedy problems created by the ai systems
[00:21:40.680]they procured from those third party vendors.
[00:21:43.180]This accountability gap then means that algorithmic systems
[00:21:46.730]are responsible for government decision making,
[00:21:49.180]without any mechanism for accountability or liability.
[00:21:55.320]Stated differently according to Kitchin
[00:21:57.560]in a relatively recent piece and these are his words,
[00:22:00.763]" We are now entering an era of widespread
[00:22:03.110]algorithmic governance wherein algorithms play
[00:22:05.910]an increasing role in the exercise of power,
[00:22:08.390]a means through which to automate the disciplining
[00:22:10.950]and controlling of societies,
[00:22:12.760]and to increase the efficiency of capital accumulation."
[00:22:16.900]End quote and yet, what we generally lack as a public,
[00:22:21.000]according to Diakopoulos is clarity
[00:22:23.660]about how algorithms exercise their power
[00:22:25.980]over us so, moving forward,
[00:22:29.300]how do we assure transparency and accountability
[00:22:33.200]in these algorithmic systems?
[00:22:35.790]In recent years, we've seen the advent of research
[00:22:38.230]Institute's dedicated to this topic.
[00:22:41.010]There seems to be a widespread recognition
[00:22:43.310]that corporations who control many of these proprietary
[00:22:45.810]algorithms, and much of the underlying data
[00:22:48.930]are not going to police themselves.
[00:22:51.170]In the interest of time we'll share
[00:22:52.520]just a few notable examples.
[00:22:56.550]The AI Now Institute at New York University
[00:22:58.740]was co founded by Meredith Whittaker ,
[00:23:01.220]Whittaker notably, previously founded Google's open research
[00:23:04.590]group and was a court organizer in the Google walkouts
[00:23:08.410]Ai Now and this is their mission statement
[00:23:10.560]is an interdisciplinary research center
[00:23:13.100]dedicated to understanding the social implications
[00:23:15.740]of artificial intelligence.
[00:23:17.590]The Institute has been issuing various reports
[00:23:20.120]some of which you see here is just a smattering.
[00:23:23.500]Also, individuals that are part of the Institute
[00:23:25.900]have been testifying before the United States
[00:23:28.100]House of Representatives Committee on Oversight
[00:23:30.410]and Reform, and they've been publishing various legal
[00:23:33.560]and technical academic journals in various journals.
[00:23:39.160]Similarly, the Ada Lovelace Institute in London
[00:23:41.910]is an independent research and deliberative body
[00:23:43.990]with a mission to ensure data
[00:23:45.820]and ai work for people and society.
[00:23:48.980]They've been pursuing similar strategies,
[00:23:51.360]trying to make the case for the social value of data
[00:23:54.260]and addressing the asymmetries of power and data justice.
[00:23:57.880]Their approach is focused on changing regulations
[00:24:00.860]as you can see here, changing narratives
[00:24:03.550]and changing practices.
[00:24:07.490]It's also worth noting that our neighbor to the north
[00:24:09.490]recently embarked on a Pan Canadian artificial intelligence
[00:24:13.050]strategy and in Europe, there are moves to use
[00:24:16.380]the e-use of general data protection regulation or GDPR,
[00:24:20.540]to address transparency,
[00:24:22.210]and help regulate algorithmic systems.
[00:24:24.680]In the US, it seems most of the activity in this area
[00:24:27.710]is coming not from the government
[00:24:29.530]but from advocacy groups and research centers,
[00:24:31.630]like I mentioned, the Ai Now Institute.
[00:24:37.320]One thing is clear, algorithmic systems,
[00:24:41.330]both hard to say are advancing rapidly,
[00:24:44.780]with tremendous promise
[00:24:46.160]and tremendous peril in equal measure.
[00:24:49.040]We hope this presentation has given you all a sense
[00:24:51.040]of the breadth and complexity for how algorithms
[00:24:54.340]are being applied, and what challenges lie ahead
[00:24:56.900]for all of us.
[00:24:59.190]And in closing, we'd like to share three important
[00:25:01.620]and optimistic considerations issued
[00:25:04.520]by the Ada Lovelace Institute
[00:25:05.950]in their rethinking data report.
[00:25:09.810]So they're calling upon all of us, as actors in society
[00:25:14.020]to consider the following:
[00:25:16.780]how data might narrow any inequalities
[00:25:18.690]rather than widen them.
[00:25:21.600]How data might tackle power imbalances
[00:25:24.430]rather than entrench them.
[00:25:27.595]And how data might help us address the global issues
[00:25:29.830]of resource allocation, climate change and migration,
[00:25:33.320]rather than exacerbate them
[00:25:36.193]and with that thanks very much for your attention.
[00:25:38.717](clapping)
[00:25:49.263]That's it?
[00:25:51.750]Heather has to turn on her leg
[00:25:53.117]There we go (chuckling)
[00:25:55.950]All right, are there any questions?
[00:26:00.730]From anyone in the audience?
[00:26:05.058]Yes?
[00:26:06.812]You gave us the perspective
[00:26:07.645](mumbles) having control over ai technology
[00:26:13.534]in what way can the everyday person kind of take back,
[00:26:16.597]in what way do we have power
[00:26:18.250]ourselves to influence (mumbles) ai
[00:26:20.811](mumbles)
[00:26:24.860]Great question.
[00:26:27.339](mumbles)
[00:26:29.055]You wanna start and then I'll take over
[00:26:31.919]Can you repeat the question
[00:26:32.752]so that everybody can hear it?
[00:26:34.160]So the question was related to, we're hearing
[00:26:39.490]about how big corporations, organizations,
[00:26:41.600]et cetera are using big data potentially in negative ways.
[00:26:45.870]So how as an everyday person can we counter
[00:26:48.840]that and what strategies might be used to do so?
[00:26:53.860]So to begin to address that one,
[00:26:56.620]I think we could reiterate what Carrie
[00:26:58.610]had said near the end right?
[00:27:00.150]It sort of joined some advocacy groups, right?
[00:27:04.360]Be aware of what's going on and that's really the first
[00:27:06.700]thing is to, you know, step back, reflect
[00:27:09.190]and think about what's happening,
[00:27:10.470]like when you're using social media,
[00:27:13.010]whether it's Facebook, Instagram
[00:27:14.240]or whatever it might be thinking
[00:27:15.660]about what it is that you're seeing
[00:27:17.070]and what it might impact it might have
[00:27:19.220]maybe on you individually or in a larger sense right?
[00:27:22.030]So that idea of being self reflective,
[00:27:24.650]potentially being proactive in ways
[00:27:29.051]you could become a computer programmer, I guess.
[00:27:32.346](chuckling) right?
[00:27:33.430]In addition to bringing your humanities background
[00:27:35.630]cross the two together and sort of address
[00:27:38.590]algorithmic development in alternative ways
[00:27:42.007]and alternative perspectives so.
[00:27:43.990]I mean I think I'll just add to that, I think
[00:27:47.880]demanding greater transparency
[00:27:50.790]in how these things are working
[00:27:53.060]is one thing that we can we can do, I think
[00:27:57.200]that companies that are using algorithms that may impact
[00:28:02.310]our daily lives in positive and negative ways
[00:28:05.390]are aloof to release those and frankly,
[00:28:09.050]they might be a bit opaque to us anyway
[00:28:11.710]if we did actually see all of the source code,
[00:28:14.620]but to reiterate Heather's point,
[00:28:16.350]I think that getting involved,
[00:28:18.630]being aware, educating yourself about all the ways
[00:28:21.080]these are seeping into our lives is one way to start.
[00:28:24.690]And in some, relatively,
[00:28:27.300]it might seem like relatively simple studies
[00:28:29.390]have actually led to scale changes,
[00:28:31.920]you know, people have done things like just looking
[00:28:34.130]for how for instance how when you Google search
[00:28:38.100]something it auto fills right?
[00:28:39.763]What it thinks you're gonna be asking for just by starting
[00:28:42.850]to ask questions about like, "Okay, well, if I'm logging in
[00:28:45.900]from home or from the state of Nebraska,
[00:28:48.490]what does that look like?"
[00:28:49.370]And then asking the same question actually,
[00:28:51.100]when they are somewhere else, to try to start
[00:28:53.790]to unravel what it is the algorithm thinks
[00:28:57.890]it knows about you and then studies have been built
[00:29:00.450]upon those kinds of observations
[00:29:02.580]to make larger scale changes
[00:29:04.040]or the observations about how these are impacting our lives?
[00:29:12.205]Question over here?
[00:29:14.518]My question is from biases,
[00:29:18.327]color discrimination so we know the people who created
[00:29:23.352](mumbles) to (mumbles) about discrimination,
[00:29:26.701]that they have to do with the people
[00:29:28.173]who create (mumbles)?
[00:29:30.533]Who did not do that in (mumbles) why
[00:29:32.738]because I believe machines only produce
[00:29:36.450]what we (mumbles) on them
[00:29:38.999]so could these biases be also be with the creators
[00:29:42.079]of these algorithms?
[00:29:44.328]And how can we change that? Absolutely,
[00:29:46.130]though I think that's absolutely right.
[00:29:47.840]I mean, Heather's on top of one of her earlier slides said,
[00:29:51.840]you know, these are opinions embedded in code, right?
[00:29:55.150]And so I think you're right to point out that these biases
[00:29:58.680]came from people right?
[00:30:00.196]That they didn't just fall out of the sky, right?
[00:30:02.640]And so we need to be attentive
[00:30:04.270]to that, who are the programmers?
[00:30:05.770]What are the demographics of the people
[00:30:07.517]who are writing these codes?
[00:30:09.700]And how do we change that?
[00:30:11.320]How do we change, how to do we expand that?
[00:30:14.250]One of the things that the folks at the AI Now Institute
[00:30:17.260]and other places have been pointing out is precisely
[00:30:20.470]that disparity in terms of who's controlling
[00:30:24.860]these algorithms?
[00:30:25.693]Who's writing them?
[00:30:27.660]Who's (mumbles)fortunately impacted by them?
[00:30:29.870]And that we need to change address that power imbalance
[00:30:32.650]within the tech industry as well.
[00:30:36.050]So I don't know if that answers your question?
[00:30:37.150]But you're absolutely right,
[00:30:38.270]that we need to change who's behind the machine
[00:30:41.910]to be able to be more attentive to these issues.
[00:30:44.760]In addition, it relates to the data
[00:30:47.140]that we have the data that's on hand
[00:30:49.680]that's in the databases, right?
[00:30:51.200]And what's being used to train sort of machine learning,
[00:30:54.790]or ai and so the data we have is from certain
[00:31:01.260]socio economic classes or different parts of the country
[00:31:03.910]or different parts of the world
[00:31:05.030]and it's not equitable right?
[00:31:08.210]Across the globe and so that it also impacts
[00:31:11.890]sort of the outcomes that we're getting
[00:31:14.040]from the algorithms in addition to the bias
[00:31:16.140]maybe perhaps written into them so.
[00:31:22.478]Yes? (mumbles)
[00:31:23.980]to that if they're producing
[00:31:26.930]discriminatory of bias results in case we have only bias
[00:31:31.022]data to work with, how can we continue to use algorithms
[00:31:35.767]to handle problems that they don't have the consciousness
[00:31:41.266]to understand that they cannot understand
[00:31:44.470]that the data itself is bias
[00:31:46.908]and there's no way for an algorithm (mumbles)
[00:31:49.108]to mend that so how can you continue using them for almost
[00:31:53.052]any societal problem when our data is (mumbles)
[00:31:58.170]Right well so the question relates to how can we continue
[00:32:02.260]to use algorithms right?
[00:32:05.030]If the data that's going into them is potentially biased.
[00:32:09.400]And it would say that all data or biased, right?
[00:32:14.030]And I think you're really clear to point
[00:32:16.240]out that yeah, the algorithm itself, the computer
[00:32:18.810]itself, can't make those changes necessarily, right?
[00:32:22.310]We have to look at and be self reflective,
[00:32:25.350]as a society and as individuals and sort of see
[00:32:28.330]what those biases might be in the data
[00:32:30.310]and make light of them and certain changes have occurred.
[00:32:35.020]As Carrie pointed out with some healthcare industries
[00:32:36.980]and others, and when people become aware of those things,
[00:32:39.030]you can rewrite the algorithms so for example
[00:32:41.750]to take out health costs out of an algorithm in that sense.
[00:32:48.780]Yeah and just to add, I would (mumbles) to guess
[00:32:51.620]that you know it was some of the ones
[00:32:53.750]that we've been critical of here today,
[00:32:55.800]would simply point out that with time
[00:32:58.120]and with more data input these will improve right?
[00:33:00.270]And this will ameliorate some of the concerns
[00:33:02.980]that have been brought to light,
[00:33:04.670]the challenge though of course is the the fact
[00:33:06.700]that the billions of dollars of investment and the pace
[00:33:10.150]of this is far outstripping or outpacing our ability
[00:33:14.640]to be some sort of checks and balance
[00:33:16.550]about the unintended consequences of these algorithms.
[00:33:26.588][Deep Voiced Student] Is there a list
[00:33:27.421]of references that you all sighted available?
[00:33:31.660]We can make them available.
[00:33:32.790]We did not include a slide
[00:33:34.050]with them on there but are happy,
[00:33:35.800]I don't know if there are PowerPoints that then get shared
[00:33:38.000]as part of CAS Inquire but we could add
[00:33:41.020]a bibliography slide (laughing)
[00:33:42.650]that would have all of these
[00:33:43.680]on it [Deep Voiced Student] Be
[00:33:44.513]quite beautiful (mumbles) Yeah sure
[00:33:46.140]no, thanks happy to share that.
[00:33:51.645]Yeah, I'm really struck
[00:33:53.147]by sort of tension between (mumbles)
[00:33:58.412]all presented as totally revolutionary right?
[00:34:01.155]Partly because (mumbles)
[00:34:04.546]and the (mumbles) is for, well humans have always done
[00:34:07.973]that, (mumbles) to each other when new technology
[00:34:09.821]comes along and (mumbles) some more
[00:34:12.150]and there's sort of this continuity here rather
[00:34:13.517]than the total (mumbles) that you have with the good stuff
[00:34:17.087]so I was curious if you see the discrimination
[00:34:20.824]and the sort of the negative things as just continuity
[00:34:23.988]as more of the same for new technology (mumbles)
[00:34:25.809]do something revolutionary in the negative side as well
[00:34:30.917]if there's (mumbles)
[00:34:32.547]Swear we have to put on our archeological
[00:34:33.838]hats Yeah (chuckling)
[00:34:36.132]how about this one?
[00:34:38.407]I mean I wouldn't say that
[00:34:41.800]it's interesting to make that observation,
[00:34:44.340]given our different components brought together
[00:34:47.010]so I appreciate that piece of feedback
[00:34:48.820]in terms of how these two different sets of observations
[00:34:52.680]were presented here today
[00:34:54.950]I as an archeologist I certainly think that these biases
[00:34:58.730]have always been there and that what has changed,
[00:35:03.700]in my mind is the scale of their impact right?
[00:35:09.434]You know, I study a society that is maybe five,
[00:35:12.680]10,000 people living congregated in a place
[00:35:15.660]in North Central New Mexico, right?
[00:35:17.910]That's a very different scale than the impact
[00:35:20.600]of an algorithm that goes wrong upon 10s of millions,
[00:35:24.130]if not billions of people.
[00:35:26.290](coughing)
[00:35:33.546](mumbling) (chuckling)
[00:35:36.639](mumbles) this is really interesting
[00:35:38.270]I have a question that juxtaposes my experience
[00:35:42.250]with my son whose a computer science major
[00:35:45.286]and he works in a lab and he has done this, he loves ai
[00:35:49.428]and that's what he wants to do, (mumbles) he spent
[00:35:53.320]a lot of the break in December and January doing this really
[00:36:00.157]what I consider just kind homeless archaic (mumbles)
[00:36:04.773]circling like the fish in the ocean
[00:36:07.207]because the computer has to be taught to recognize
[00:36:10.091]that this is a fish in the ocean and it's just so basic
[00:36:14.332]and fundamental, to juxtapose that which just seems
[00:36:18.239]really very fundamental, it seems so early
[00:36:22.357]in the (mumbles) fish (mumbles) intelligence area
[00:36:24.910]to all the concerned about like Google and Facebook
[00:36:29.021]and Face Recognition Efforts at China for example
[00:36:33.970]I mean it seems like the research (mumbles)
[00:36:36.656]cause he's working in a research lab at the University
[00:36:39.220]versus kind of this other stuff, there's such a huge
[00:36:43.983]difference between those two and I get scared
[00:36:46.530]when I hear about ai as it relates to the commercial side
[00:36:50.460]in the government (mumbles) versus what I see
[00:36:53.390]my son doing (chuckling)
[00:36:55.058](mumbles) and I just wonder if what your thoughts are.
[00:36:59.830]I mean, there's probably various ways
[00:37:03.090]to think about that, I mean,
[00:37:04.530]there is a difference, often with the scale
[00:37:07.910]of what's happening at a research level, right?
[00:37:10.410]And what can be funded by large organizations.
[00:37:14.260]I think part of that relates to when research,
[00:37:16.800]I think, as scientists, we step back, we reflect
[00:37:21.784]we often we'll take more care to look at our data,
[00:37:26.090]to look at our algorithms, to look at what's happening,
[00:37:28.390]and be clear about what's happening
[00:37:29.730]rather than sort of the end goal is somewhat
[00:37:32.900]maybe but maybe not the end goal is different
[00:37:34.490]but the process is much different
[00:37:36.320]and then often with larger corporations and organizations,
[00:37:39.720]there's the profit maybe what they're searching for
[00:37:42.800]when they're getting, you know, that's what they're trying
[00:37:45.020]to get and so you often don't have that slow,
[00:37:47.970]reflective, methodological iterative reflection
[00:37:51.520]on what you're doing and you kind of throw it out
[00:37:53.150]there and then that potentially can be it is more scary
[00:37:58.330]and dangerous, right?
[00:37:59.163]And so what we're doing and it relates to one
[00:38:00.500]of the other questions in some senses
[00:38:01.910]is we're letting the public and people
[00:38:03.930]be our guinea pigs, right?
[00:38:05.490]Oops that didn't work right.
[00:38:06.630]So how many people were impacted when we come back
[00:38:08.880]and go oops let's change that variable?
[00:38:10.970]So that's I think one side.
[00:38:14.360]The thing that, your comment made me think
[00:38:16.020]about is that and I just know a little bit
[00:38:18.990]about that but Google has gotten in trouble in recent,
[00:38:23.816]last year so with regard to lower paid contract employees
[00:38:29.600]versus full time, people with benefits, right?
[00:38:33.437]And that there's just this huge army of Google
[00:38:36.540]kind of contractual hourly workers
[00:38:39.370]and my sense is that what Google has those people doing
[00:38:42.950]in part on the algorithmic side
[00:38:45.020]is doing exactly the same kind of training work
[00:38:47.020]that you're son is working on
[00:38:49.770]and that, you know, so there's this disparity
[00:38:52.350]too in terms of the labor and the impact and the outcomes,
[00:38:56.230]in terms of how this is being approached
[00:38:58.880]by their staff, employees.
[00:39:05.772]We had Antia, you had.
[00:39:08.867]To seek to share
[00:39:09.830]my skepticism about regulation ever being
[00:39:15.030]a pathway in the US where people are notoriously
[00:39:18.550]sort of skeptical about regulations
[00:39:21.430]for some of the very reasons you've talked about
[00:39:23.890]but half the watch groups in the US we made
[00:39:27.973]any attempt to sort go that route or push
[00:39:31.590]towards that or is the sort of concern that regulation
[00:39:35.240]itself will sort of override
[00:39:38.000]so it's too overpowering for them to even
[00:39:42.110]it seems to be an area that cost calls
[00:39:45.430]for some form of at least sort of writing it into law
[00:39:50.170]that people provide open access and open sources right?
[00:39:55.620]So then you could look and it would probably be
[00:39:59.140]a disinsentive to people like Google
[00:40:02.336]if they had to just make everything public
[00:40:05.625]then it might not be yearly as profitable
[00:40:08.020]to do all of this so (mumbles)--
[00:40:10.537]I mean I'm heartened when somebody like Whittaker
[00:40:14.820]is asked to come to Washington and testify right?
[00:40:18.670]So I'm heartened by that, an expert
[00:40:20.940]that's been on the Google commercial side
[00:40:23.130]and is now on the non-profit advocacy side
[00:40:26.010]so I find that heartening, I think also the case
[00:40:29.063]that in my observation the place that we're seeing
[00:40:32.340]regulation come to the floor is more at the state level.
[00:40:35.320]There was a case in New York City about housing
[00:40:37.180]discrimination using biometric recognition
[00:40:40.220]and then there was a pushback and a task force
[00:40:42.643]and kind of got close to regulation and then didn't
[00:40:46.290]but I think there are cases about ai regulation making
[00:40:50.180]it into a state law in Oregon, and in Michigan,
[00:40:55.560]and of course in reaction to some things
[00:40:58.760]that went terribly wrong.
[00:41:03.886]Yeah (mumbles)
[00:41:06.357][Foreign Female Student] I'm curious
[00:41:07.190]about what is the (mumbles)
[00:41:08.777]the conversation that's come about before
[00:41:11.424]about the fact that diversifying some fields
[00:41:14.930]of work can help to prevent those biases
[00:41:18.660]or like change the data that we put into the algorithms
[00:41:22.692]so they're not bias and I was wondering, how far along
[00:41:26.825]do you think that we are too diversifying more
[00:41:30.430]those fields so we have less of those biases?
[00:41:35.840]That's a great question and I think
[00:41:39.132]that that would have some potentially positive impacts
[00:41:44.540]if we were able to sort of bring in wider perspectives
[00:41:47.040]and more diverse perspectives.
[00:41:50.730]In terms of I think there's an awareness
[00:41:53.550]of that you'll see headlines periodically
[00:41:59.180]saying, you know Apple is looking for not simply
[00:42:03.950]computer programmers, but computer programmers
[00:42:06.170]with a cultural awareness, understanding,
[00:42:08.010]a human humanities background, which is great
[00:42:10.630]that's the first kind of at least there's awareness
[00:42:13.370]on that level but it comes again, I think back to scale
[00:42:17.280]in some sense it is when it comes down to sort of getting
[00:42:23.200]one sort of getting the profit out and getting things done,
[00:42:25.740]right, maybe mass producing sort of in the more traditional
[00:42:28.800]way, but hopefully through time, policies
[00:42:32.100]and cultural practices and organizational practices
[00:42:35.630]will change right to be more diversified,
[00:42:38.820]include more diverse biases through advocacy,
[00:42:41.910]through regulation, through other kinds of means.
[00:42:46.990]Yeah I'll just add that I mean, I don't have the numbers
[00:42:51.686](mumbles) are they're ready to be able
[00:42:53.910]to give you a clear response
[00:42:56.050]to how that's changed over time
[00:42:57.550]and have we reached some sort of point that we're all happy
[00:43:00.790]with right that we're feeling like people
[00:43:02.360]are really represented at the highest levels of decision
[00:43:05.730]making in these contexts
[00:43:09.190]but I think that's one of the cases
[00:43:11.500]where people within the general public have an opportunity
[00:43:14.730]to educate themselves about what do those trends look like,
[00:43:17.717]demand to have access to those kinds of numbers
[00:43:21.240]and are the employees, the kind of contractual level
[00:43:25.090]sort of within the Google model, right?
[00:43:26.373]That these are hourly employees versus full time paid staff
[00:43:30.010]like what are those numbers and demographics look like?
[00:43:33.020]At those two different scales and I think
[00:43:35.840]too that's where a lot of these outside research institutes
[00:43:40.180]like AI Now and Ada Lovelace and others,
[00:43:44.230]have a critical role to play in terms of gathering
[00:43:47.940]those data, and helping to share
[00:43:50.610]that with all of us so we know how far we have to go
[00:43:54.290]and where are the areas that we need
[00:43:57.955]to concentrate our efforts.
[00:44:02.293](coughing) Sort of
[00:44:05.539](mumbles) you think (mumbles)
[00:44:08.853]kind of come out or do you think it'll be kind of harder
[00:44:12.784]or would be less difficult in the sense of changing
[00:44:17.721]those or updating those algorithms due to people
[00:44:22.915]who create in the sense of like the whole
[00:44:25.458]racial discrimination (mumbles) like medical,
[00:44:28.638]hiring like all the different (mumbles)
[00:44:32.276]would you guys (mumbles) or (mumbles)
[00:44:39.302]and keeping it current (mumbles)
[00:44:45.218](mumbles) I'm trying to understand the question.
[00:44:46.051]So the question is then with more data,
[00:44:49.145]do we, So
[00:44:52.352]Yeah sorry, thank you
[00:44:53.544]Yeah, yeah thanks (mumbles)
[00:44:56.091]Sorry I (mumbles) right now
[00:44:58.667]basically (mumbles) algorithms
[00:45:00.655]in the first place, overtime with data like,
[00:45:05.221](clearing throat) updates and gets more accurate
[00:45:08.275]per se, do you (mumbles) the algorithms
[00:45:11.187]like in what they teach in the (mumbles) companies
[00:45:14.043]do you think they'll be easier to regulate
[00:45:16.826]or necessarily take those biases
[00:45:19.417]away towards racial profiling towards (mumbles)
[00:45:22.803]what the medical practices and everything?
[00:45:25.114]Do you think it'll be easier over time
[00:45:26.543]or it might be more difficult to do this
[00:45:29.242]you know, practice it if they keep teaching
[00:45:31.023]them the traditional (mumbles)
[00:45:32.769]or making it better (mumbles)?
[00:45:37.403]I think there's this strong potential with more data
[00:45:40.850]and more inclusivity right?
[00:45:44.030]Including more data for more diverse parts of the world
[00:45:48.740]and diverse populations that potentially
[00:45:54.140]some of the algorithms may in one hand, work differently
[00:45:58.050]and of course have different outputs right?
[00:46:01.474]With a more diverse data on the other side, yes
[00:46:03.700]so as you're saying the algorithms
[00:46:05.340]themselves potentially need to be changed
[00:46:07.480]because the variables that are being used
[00:46:09.730]and so I think that definitely, I would be more positive
[00:46:13.840]than negative in the sense and I think definitely
[00:46:16.810]the potential exists.
[00:46:18.350]The question is where you're at in the world, potentially,
[00:46:21.760]how, it might be used differentially in different countries
[00:46:24.010]and based on different regulations,
[00:46:27.500]social political systems, ideologies, et cetera right?
[00:46:30.820]How that may influence the way algorithms are controlled
[00:46:34.970]and who is operating them and to what extent
[00:46:38.150]they have a large impact or not.
[00:46:41.730]Heather wore yellow cause she's a more positive
[00:46:43.850]person than me (chuckling)
[00:46:45.430]and I wore black, so we color coded
[00:46:47.990]ourselves, I guess my concern,
[00:46:50.520]I'd like to be optimistic that yes, over time,
[00:46:52.930]these things will improve and perhaps they will.
[00:46:55.950]The challenge though, of course,
[00:46:57.230]is that we're not allowed to know how these decisions
[00:47:01.680]You know, what the black box is doing?
[00:47:04.691]Then it's hard to know years hence, is it better?
[00:47:08.930]Is it worse right?
[00:47:10.430]And some of the cases that we profiled here today,
[00:47:13.750]were highlighted only because people, you know, gathered
[00:47:17.430]the original data and actually challenged
[00:47:21.930]is this doing what people say it's doing?
[00:47:24.720]If you go to the HireVue hiring page
[00:47:27.030]and listen to the pitch
[00:47:29.080]about what that assessment tool will do in terms of hiring
[00:47:33.840]practices, it's gonna move heaven and earth right?
[00:47:37.820]It's gonna equalize everyone,
[00:47:40.120]you're gonna interview more people,
[00:47:41.210]you're gonna diversify your workforce
[00:47:42.970]and so on, how do we know? (chuckling)
[00:47:46.790]How do we hold them accountable
[00:47:49.210]that's actually what it's doing?
[00:47:50.900]As opposed to simply creating,
[00:47:52.860]as I said, an automated form of discrimination
[00:47:56.230]in terms of who's getting hired
[00:47:57.440]and who has the higher employability score.
[00:48:04.494]Yeah
[00:48:05.327](mumbles)
[00:48:06.560]Sure (chuckling)
[00:48:07.462](mumbles) (chuckling)
[00:48:10.004](clearing throat) What is this litigation over
[00:48:11.715]this topic (mumbles), I'm thinking about the (mumbles)
[00:48:15.762]to play out the couple of ways to finding
[00:48:17.229]genuine material when I wanna use
[00:48:18.605]that (mumbles) in a corporation
[00:48:22.204]and a professional association (mumbles) building
[00:48:25.184]with it more liable to (mumbles)
[00:48:28.858]so this is more like the development of these tools
[00:48:31.020]is more than halfway to an (mumbles)
[00:48:32.590]back in (mumbles) like medicine
[00:48:34.804]or something like that may be an analogy
[00:48:37.100]and so is this gonna be saddled with like malpractice
[00:48:39.325]like lawsuits?
[00:48:40.419]Or you see litigation going along that front
[00:48:43.582]or is it never gonna get to litigation?
[00:48:46.789]Yeah I think it's already being litigated
[00:48:48.420]and I think that's where we're seeing Taantia's question,
[00:48:50.570]the pressure for greater regulation or task force oversight
[00:48:53.970]because in some of these high profile cases
[00:48:56.730]you have, like the government themselves, the City Council,
[00:49:00.910]or whomever throwing up their hands and saying,
[00:49:03.323]" Well we didn't know, we didn't understand
[00:49:05.300]how this is gonna work, you can't hold us responsible
[00:49:07.470]because we were ignorant (chuckling) right?
[00:49:09.700]Blame them, blame the third party vendor right?
[00:49:12.310]And so I do think that that's part of the reason
[00:49:15.430]why the folks for instance at Ai Now
[00:49:18.330]are positioning these sought pieces
[00:49:20.620]and co authoring with legal scholars
[00:49:23.870]in places like the Columbia Law Review
[00:49:26.420]to get that message out.
[00:49:32.983]As a fellow archeologist,
[00:49:34.370]I have a very general question,
[00:49:35.895](chuckling)
[00:49:36.728]We are very interested in political systems and (mumbles)
[00:49:41.022]that initially we thought (mumbles) all of these algorithms
[00:49:44.928](mumbles) equalizer as you said but then we see
[00:49:48.960]there is a (mumbles) that couldn't leave (mumbles)
[00:49:57.969]So I just wonder what are your thoughts
[00:50:00.136]whether (mumbles)
[00:50:08.383](chuckling)
[00:50:10.120]That's a big question.
[00:50:11.550](chuckling) Yeah it is a big question.
[00:50:14.850]Firstly, I don't think I have any heavy observations
[00:50:18.250]about that I guess I'm in the same spirit
[00:50:20.690]as we mentioned a few minutes ago,
[00:50:23.120]I guess I'm heartened that there are really smart people
[00:50:28.530]who are holding governments accountable
[00:50:31.670]and are pushing as hard as they can to try
[00:50:37.480]to hold the political systems,
[00:50:40.180]the powers that be accountable if and when they choose
[00:50:43.880]to use algorithmic systems in terms of automating governance
[00:50:49.610]and so I guess I'm hopeful that I don't know
[00:50:54.080]I don't know if your question is leading
[00:50:55.190]towards like do we think there's a revolution
[00:50:56.730]coming? (chuckling)
[00:50:58.610]I don't know but I guess I am taking solace from the fact
[00:51:03.411]that there are a lot of smart people
[00:51:04.943]that are working very hard
[00:51:06.280]to hold these governments accountable,
[00:51:11.180]I mean yeah, just along those lines,
[00:51:13.032]I just think it's important to think about these things,
[00:51:18.210]not yes is there's a global scale
[00:51:20.380]to it but there's also local scales
[00:51:23.220]of these in various countries throughout the world
[00:51:29.090]that are not as big a players in this right?
[00:51:32.310]And they have different social political systems currently
[00:51:35.840]already and so I think when we think
[00:51:37.740]about the impacts of algorithms on the political level,
[00:51:41.500]we have to sort of delve a little deeper
[00:51:43.730]into the more local scale to think
[00:51:45.180]about how that impact may be at different places.
[00:51:54.474]Yeah? Jim?
[00:51:56.100]To what extent from your perspective
[00:52:00.666]is ai limited by definition to what it could do in (mumbles)
[00:52:06.840]it is, ai really is a product of binary logic
[00:52:11.994]and to what extent are you limited to applying binary
[00:52:16.250]logic (mumbles)
[00:52:18.994]Is there a limitation
[00:52:21.275]that defies the maximum use,
[00:52:29.380]the maximum application of ai
[00:52:33.770]as opposed to an assumption that (mumbles) out there that ai
[00:52:37.753]can form us an (mumbles) on a scale
[00:52:41.197]much wider than the (mumbles)?
[00:52:46.058]That's a great, it's a great question.
[00:52:47.900]I mean, I would say, just as a more personal response,
[00:52:51.940]yeah, I think you're limited (chuckling) right?
[00:52:53.720]I mean, I think you know, humans are not zeros and ones.
[00:52:56.560]We're not binary and sort of proves the underlying theme
[00:53:02.310]of part of this right?
[00:53:03.880]Talk is that humans in the beginning are the ones
[00:53:07.680]programming and creating these and so that's always going
[00:53:11.320]to be sort of the underlying basis of it now that may morph
[00:53:16.020]into many different ways depending on how algorithms
[00:53:20.350]are rewritten based off of training sets
[00:53:23.170]but I don't think that, I think
[00:53:26.560]that they're never gonna fundamentally
[00:53:28.180]if I'm getting your question right,
[00:53:30.680]either, you know, replace humans or become the same
[00:53:33.750]as human decision makers-- (mumbles)
[00:53:37.112]More restricted to the question that ai,
[00:53:40.762]the assumption is ai could be function (mumbles)
[00:53:44.600]so unless limited to (mumbles)
[00:53:48.661]what areas can ai truly beat function (mumbles)?
[00:53:52.685]And what are the limitations
[00:53:55.399]because of it's binary structure, to what extent
[00:54:00.887]can it (mumbles) regardless of how much it continues
[00:54:04.682]to develop (mumbles)
[00:54:07.150]I don't know that we know the answer to that yet
[00:54:09.670]because it's still relatively early days
[00:54:11.800]in terms of the broad scale application
[00:54:14.750]of these algorithmic systems in a whole host
[00:54:18.200]of areas of life, you know I,
[00:54:24.110]a couple of things come to mind
[00:54:26.030]in response to your question,
[00:54:27.050]I guess there was like a 1950s dystopian novel
[00:54:31.590]you guys can correct me, I'm sure there are people
[00:54:33.620]in the audience that are more astute
[00:54:35.330]about this than I am by Alan Turing who talks about
[00:54:38.500]how computer systems there's only a matter of time
[00:54:42.300]before they are our overlords
[00:54:45.560]and I do think based on the work that you know
[00:54:48.600]the research that we have done that there is an expectation
[00:54:55.090]out there that automation whether it's decision making
[00:54:58.500]in governments or otherwise is only gonna continue
[00:55:01.610]a pace as more and more investment goes
[00:55:05.490]into these and the other thing,
[00:55:07.730]I'll just say in response to your question Jim,
[00:55:09.810]is there's something else that comes to mind
[00:55:13.240]that you know, in developing these algorithms,
[00:55:16.670]we as humans define objectives
[00:55:19.060]and it's been the observations of computer scientists
[00:55:21.830]that we are terrible (chuckling)
[00:55:24.010]at defining those objectives and so we have an ill defined
[00:55:27.200]objective that we feed into a computer system,
[00:55:30.570]and then it kind of spirals out right?
[00:55:34.050]And that's where this kind of enchanted determinism
[00:55:36.690]no one's quite sure what's happening under the hood,
[00:55:39.040]after more and more training data are going
[00:55:41.480]in and the algorithms being refined over time
[00:55:44.500]by the system in ways that we don't quite understand.
[00:55:48.180]So I guess that's my response to your question
[00:55:51.293]about what the limits are, I don't know
[00:55:53.082]that we know yet.
[00:55:55.756]Previously (mumbles)
[00:55:58.401]described (mumbles) is coming
[00:56:01.481]and I think (mumbles) is no longer binary
[00:56:06.496](mumbles) (chuckling)
[00:56:08.736]No longer limited by
[00:56:11.212]Should we wrap? Oh Priscilla,
[00:56:13.691]I'm wondering if you have some advise
[00:56:16.506]for us as we are living in this age of social media
[00:56:23.184]is probably not an option to go off the grid
[00:56:25.984](chuckling)
[00:56:27.258]But-- Though tempting.
[00:56:28.140]Practices are there things
[00:56:30.315]from your perspective that we should or should not be doing
[00:56:34.714]in order to expose us more or less
[00:56:38.203]to the kind of (mumbles)
[00:56:40.157]that might happen or are we so hopeless
[00:56:44.338](mumbles) down the road that and there's so much data
[00:56:47.241]out there that it doesn't matter
[00:56:49.216]how we (mumbles)?
[00:56:52.720]You're wearing the yellow
[00:56:53.553]sc-- (laughing)
[00:56:54.463]I guess we (mumbles) the positive side here, well,
[00:56:59.170]I do think that there is a lot more data
[00:57:02.420]about all of us than we realize, right?
[00:57:04.350]Out there that's being captured in many different ways.
[00:57:08.940]I think that we shouldn't completely just throw up our,
[00:57:14.980]throw the towels aside
[00:57:18.100]but I think that we can...
[00:57:24.180]Yeah I'm trynna get this positive spin
[00:57:26.440]here on this one (chuckling)
[00:57:28.900]I think that if I might interject,
[00:57:30.440]Sure The
[00:57:34.740]positive side is what we all do right?
[00:57:37.090]Is education, in terms of trying to understand
[00:57:39.803]how are these technologies impacting our lives,
[00:57:43.040]what are the ways in which these are bubbling
[00:57:45.450]up to the surface, whether it's in health care,
[00:57:47.540]whether it's in city, state federal governance right?
[00:57:50.940]So to educate ourselves about how those things are happening
[00:57:53.830]and what some of those pitfalls are, I think
[00:57:57.340]is really the first step.
[00:58:00.380]Like you I don't know that there's any way to really stem
[00:58:03.310]the tide in terms of what's coming
[00:58:06.830]and what's already being utilized.
[00:58:09.640]So I think the key will simply be education and advocacy
[00:58:14.310]for the issues that we care about.
[00:58:18.657](mumbles)
[00:58:20.801]Look into Taylor, Taylor (chuckling)
[00:58:24.802]Do you wanna take on more
[00:58:26.123]or Sure
[00:58:31.630]With people starting to learn
[00:58:35.199]coding and algorithm in earlier age, you know
[00:58:39.595]couple of years ago (mumbles)
[00:58:42.131]college (mumbles)
[00:58:46.790]you know there are coding books for children
[00:58:49.790](mumbles) business, what do you think
[00:58:52.941]is probably the most realistic
[00:58:56.047]I guess progression of ai (mumbles)
[00:59:04.985](mumbles) The question is either
[00:59:06.137]how far will it go?
[00:59:08.370]Is that and there's other part of the question
[00:59:10.390]is given that--
[00:59:13.058]Progression in the next several decades
[00:59:16.046]or so with the (mumbles) more insight
[00:59:19.682]about product programmings and algorithms (mumbles)
[00:59:23.851]I guess more people if able
[00:59:25.924]to program these, how far do you think
[00:59:31.345]it will likely progress in the next (mumbles)
[00:59:36.480]That's hard to answer
[00:59:37.540]but I think that if you're thinking about the use
[00:59:39.660]and sort of being engaged at earlier ages in these processes
[00:59:43.810]I think it sort of reflects back on Carrie's point
[00:59:46.120]sort of education and so if we, I don't know
[00:59:48.730]about how far are they gonna go
[00:59:51.280]but actually what can they, how can we tailor
[00:59:54.950]them to a more positive kind of way, right?
[00:59:57.410]And so it's the what they can do and where they can go.
[01:00:01.460]Hopefully, we as ethical society,
[01:00:04.430]and people can sort of control that a little bit
[01:00:06.710]through education and awareness in some way so.
[01:00:11.156]In terms of where they're gonna go you know
[01:00:14.035]that is a hard question (chuckling)
[01:00:15.956]And I know I'm heartened that there are more and more
[01:00:18.860]young people who are growing up,
[01:00:21.100]understanding and being more sophisticated
[01:00:23.100]consumers of information and understanding the technologies
[01:00:25.930]that underlie so many of these things we're interacting
[01:00:28.930]with on a daily basis but like Heather,
[01:00:31.100]I don't know that I would prognosticate
[01:00:35.070]about where things might end up
[01:00:37.620]but, you're the future (chuckling)
[01:00:43.412]Thank you so much for coming out today
[01:00:44.938]and hoping to see you all at the end of March
[01:00:47.413]for a panel discussion
[01:00:48.970](mumbles) (clapping)

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"Discriminatory Algorithms and Cultural Complexities" | CAS Inquire

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