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CTSP Alumni Updates

September 27th, 2018

We’re thrilled to highlight some recent updates from our fellows:

Gracen Brilmyer, now a PhD student at UCLA, has published a single authored work in one of the leading journals in archival studies, Archival Science: “Archival Assemblages: Applying Disability Studies’ Political/Relational Model to Archival Description” and presented their work on archives, disability, and justice at a number of events over the past two years, including The Archival Education and Research Initiative (AERI), the Allied Media Conference, the International Communications Association (ICA) Preconference, Disability as Spectacle, and their research will be presented at the upcoming Community Informatics Research Network (CIRN).

CTSP Funded Project 2016: Vision Archive


Originating in the 2017 project “Assessing Race and Income Disparities in Crowdsourced Safety Data Collection” done by Fellows Kate Beck, Aditya Medury, and Jesus Barajas, the Safe Transportation and Research Center will launch a new project, Street Story, in October 2018. Street Story is an online platform that allows community groups and agencies to collect community input about transportation collisions, near-misses, general hazards and safe locations to travel. The platform will be available throughout California and is funded through the California Office of Traffic Safety.

CTSP Funded Project 2017: Assessing Race and Income Disparities in Crowdsourced Safety Data Collection


Fellow Roel Dobbe has begun a postdoctoral scholar position at the new AI Now Institute. Inspired by his 2018 CTSP project, he has co-authored a position paper with Sarah Dean, Tom Gilbert and Nitin Kohli titled A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics.

CTSP Funded Project 2018: Unpacking the Black Box of Machine Learning Processes


We are also looking forward to a CTSP Fellow filled Computer Supported Cooperative Work conference in November this year! CTSP affiliated papers include:

We also look forward to seeing CTSP affiliates presenting other work, including 2018 Fellows Richmond Wong, Noura Howell, Sarah Fox, and more!

 

Article published in Nature

September 22nd, 2018

Dr. Blumenstock’s article, “Don’t forget people in the use of big data for development,” was published in the journal Nature

Blumenstock receives Hellman Award

September 20th, 2018

Prof. Blumenstock was named as a 2018 Hellman Fellow for his project, “Evaluating Community Cellular Networks: How Does Mobile Connectivity Affect Isolated Communities?”

Professor Deirdre K. Mulligan and PhD student (and CTSP Co-Director) Daniel Griffin have an op-ed in The Guardian considering how Google might consider its human rights obligations in the face of state censorship demands: If Google goes to China, will it tell the truth about Tiananmen Square?

The op-ed advances a line of argument developed in a recent article of theirs in the Georgetown Law Technology Review: “Rescripting Search to Respect the Right to Truth”

To view the full article I wrote about the Impact of Oracle v. Rimini on Data Professionals and the Public, please visit:

http://blogs.ischool.berkeley.edu/w231/2018/01/23/oracle-v-rimini/

Of particular interest to Data Scientists was the question of whether using “bots and scrapers” for automated collection of data was deemed a violation of the law if it violated a Terms of Service.  An important tool in the Data Scientists’ and Data Engineers’ toolbox, automated scraping scripts provide for efficient accumulation of data.  Further, many individuals cite instances of Terms of Service being too broad or vague for interpretation.

Among the applications of these scraped data, it subsequently can be used for academic research or used to develop novel products and services that connect disparate sets of information and reduce information asymmetries across consumer populations (for example, search engines or price tracking).  On the other hand, sometimes malicious bots can become burdensome to a company’s website and impact or impede their operations.

In June of 2018, the Algorithmic Fairness and Opacity Working Group (AFOG) held a summer workshop with the theme “Algorithms are Opaque and Unfair: Now What?.” The event was organized by Berkeley I School Professors (and AFOG co-directors) Jenna Burrell and Deirdre Mulligan and postdoc Daniel Kluttz, and Allison Woodruff and Jen Gennai from Google. Our working group is generously sponsored by Google Trust and Safety and hosted at the UC Berkeley School of Information.

Inspired by questions that came up at our biweekly working group meetings during the 2017-2018 academic year, we organized four panels for the workshop. The panel topics raised issues that we felt required deeper consideration and debate. To make progress we brought together a diverse, interdisciplinary group of experts from academia, industry, and civil society in a workshop-style environment. In panel discussions, we considered potential ways of acting on algorithmic (un)fairness and opacity. We sought to consider the fullest possible range of ‘solutions,’ including technical implementations (algorithms, user-interface designs), law and policy, standard-setting, incentive programs, new organizational processes, labor organizing, and direct action.

Background

Researchers (e.g., Barocas and Selbst 2016; Kleinberg et al. 2017), journalists (e.g., Miller 2015), and even the federal government (e.g., Executive Office of the President 2016) have become increasingly attuned to issues of algorithmic opacity, bias, and fairness, debating them across a range of applications, including criminal justice (Angwin et al. 2016, Chouldechova 2017, Berk et al. 2017), online advertising (Datta et al. 2018), natural language processing (Bolukbasi et al. 2016), consumer credit (Waddell 2016), and image recognition (Simonite 2017; Buolamwini and Gebru 2018).

There has been recent progress especially in understanding algorithmic fairness as a technical problem. Drawing from various formal definitions of fairness (see Narayanan 2018; Corbett-Davies and Goel 2018; Kleinberg et al. 2017), researchers have identified a range of techniques for addressing fairness in algorithm-driven classification and prediction. Some approaches focus on addressing allocative harms by fairly allocating opportunities or resources. These include fairness through awareness (Dwork et al. 2012), accuracy equity (Angwin et al. 2016Dieterich et al. 2016), equality of opportunity (Hardt et al. 2016), and fairness constraints (Zafar et al. 2017). Other approaches tackle issues of representational harms which occur when a system diminishes specific groups or reinforces stereotypes based on identity (see Crawford 2017). Proposed solutions include corpus-level constraints to prevent the amplification of gender stereotypes in language corpora (Zhao et al. 2017), diversity algorithms (Drosou et al. 2017), causal reasoning to assess whether a protected attribute has an effect on a predictor (Kilbertus et al. 2017, Kusner et al. 2017), and inclusive benchmark datasets to address intersectional accuracy disparities (Buolamwini and Gebru 2018).

These new approaches are invaluable in motivating technical communities to think about the issues and make progress on addressing them. But the conversation neither starts nor ends there. Our interdisciplinary group sought to complement and challenge the technical framing of fairness and opacity issues. In our workshop, we considered the strengths and limitations of a technical approach and discussed where and when hand-offs, human augmentation, and oversight are valuable and necessary. We considered ways of engaging a wide-ranging set of perspectives and roles, including professionals with deep domain expertise, activists involved in reform efforts, financial auditors, scholars, as well as diverse system users and their allies. In doing so, we considered models that might be transferable looking to various fields including network security, financial auditing, safety critical systems, and civil rights campaigns.

The Panels
Below is a brief summary of the panel topics and general themes of the discussion. Full write-ups for each panel are linked. Our aim in these write ups is not to simply report a chronological account of the panel, but to synthesize and extend the panel discussions. These panel reports take a position on the topic and offer a set of concrete proposals. We also seek to identify areas of limited knowledge, open questions, and research opportunities. We intend for these documents to inform an audience of researchers, implementers, practitioners, and policy-makers.

Panel 1 was entitled “What a technical ‘fix’ for fairness can and can’t accomplish.” Panelists and audience members discussed specific examples of problems of fairness (and justice), including cash bail in the criminal justice system, “bad faith” search phrases (e.g., the question, “Did the Holocaust happen?”), and representational harm in image-labeling. Panelists noted a key challenge that technology, on its own, is not good at explaining when it should not be used or when it has reached its limits. Panelists pointed out that understanding broader historical and sociological debates in the domain of application and investigating contemporary reform efforts, for example in criminal justice, can help to clarify the place of algorithmic prediction and classification tools in a given domain. Partnering with civil-society groups can ensure a sound basis for making tough decisions about when and how to intervene when a platform or software is found to be amplifying societal biases, is being gamed by “bad” actors, or otherwise facilitates harm to users. [READ REPORT]

Panelists for Panel 1: Lena Z. Gunn (Electronic Frontier Foundation), Moritz Hardt (UC Berkeley Department of Electrical Engineering and Computer Sciences), Abigail Jacobs (UC Berkeley Haas School of Business), Andy Schou (Google). Moderator: Sarah M. Brown (Brown University Division of Applied Mathematics).

Panel 2, entitled “Automated decision-making is imperfect, but it’s arguably an improvement over biased human decision-making,” describes a common rejoinder to criticism of automated decision-making. This panel sought to consider the assumptions of this comparison between humans and machine automation. There is a need to account for differences in the kinds of biases associated with human decision-making (including cognitive biases of all sorts) and those uniquely generated by machine reasoning. The panel discussed the ways that humans rely on or reject decision-support software. For example, work by one of the panelists, Professor Angèle Christin, shows how algorithmic tools deployed in professional environments may be contested or ignored. Guidelines directed at humans about how to use particular systems of algorithmic classification in low- as opposed to high-stakes domains can go unheeded. This seemed to be the case in at least one example of how Amazon’s facial recognition system has been applied in a law-enforcement context. Such cases underscore the point that humans aren’t generally eliminated when automated-decision systems are deployed; they still decide how they are to be configured and implemented, which may disrupt whatever gains in “fairness” might otherwise be realized. Rather than working to establish which is better–human or machine decision-making–we suggest developing research on the most effective ways to bring automated tools and humans together to form hybrid decision-making systems. [READ REPORT]

Panelists for Panel 2: Angèle Christin (Stanford University Department of Communication), Marion Fourçade (UC Berkeley Department of Sociology), M. Mitchell (Google), Josh Kroll (UC Berkeley School of Information). Moderator: Deirdre Mulligan (UC Berkeley School of Information).

Panel 3 on “Human Autonomy and Empowerment” examined how we can enhance the autonomy of humans who are subject to automated decision-making tools. Focusing on “fairness” as a resource allocation or algorithmic problem tends to assume it is something to be worked out by experts. Taking an alternative approach, we discussed how users and other ‘stakeholders’ can identify errors, unfairness, and make other kinds of requests to influence and improve the platform or system in question. What is the best way to structure points of user feedback? Panelists pointed out that design possibilities range from lightweight feedback mechanisms to support for richer, agonistic debate. Not-for-profit models, such as Wikipedia, demonstrate the feasibility of high transparency and open debate about platform design. Yet participation on Wikipedia, while technically open to anyone, requires a high investment of time and energy to develop mastery of the platform and the norms of participation. “Flagging” functions, on the other hand, are pervasive, lightweight tools found on most mainstream platforms. However, they often serve primarily to shift governance work onto users without the potential to fundamentally influence platform policies or practices. Furthermore, limiting consideration to the autonomy of platform users misses the crucial fact that many automated decisions are imposed on people who never use the system directly. [READ REPORT]

Panelists for Panel 3: Stuart Geiger (UC Berkeley Institute for Data Science), Jen Gennai (Google), and Niloufar Salehi (Stanford University Department of Computer Science). Moderator: Jenna Burrell (UC Berkeley School of Information).

Panel 4 was entitled “Auditing Algorithms (from Within and from Without).” Probing issues of algorithmic accountability and oversight, panelists recognized that auditing (whether in finance or safety-critical industries) promotes a culture of “slow down and do a good job,” which runs counter to the “move fast and break things” mindset that has long defined the tech industry. Yet corporations, including those in the tech sector, do have in-house auditing teams (in particular, for financial auditing) whose expertise and practices could serve as models. Generally, internal audits concern the quality of a process rather than the validity of the “outputs.” Panelists pointed out that certain processes developed for traditional auditing might work for auditing “fairness,” as well. A “design history file,” for example, is required in the development of medical devices to provide transparency that facilitates FDA review. In the safety-critical arena, there are numerous techniques and approaches, including structured safety cases, hazard analysis, instrumentation and monitoring, and processes for accident investigation. But there are also particular challenges “fairness” presents to attempts to develop an audit process for algorithms and algorithmic systems. For one, and recalling Panel 1’s discussion, there are numerous valid definitions of fairness. In addition, problems of “fairness” are often not self-evident or exposed through discrete incidents (as accidents are in safety-critical industries). These observations suggest a need to innovate auditing procedures if they are to be applied to the specific challenges of algorithmic fairness. [READ REPORT]

Panelists for Panel 4: Chuck Howell (MITRE), Danie Theron (Google), Michael Tschantz (International Computer Science Institute). Moderator: Allison Woodruff (Google).

NOTE: this reports on an AFOG relevant ISchool final project for the Master of Information Management and Systems (MIMS) program. The project was developed by a student team composed of Samuel Meyer, Shrestha Mohanty, Sung Joo Son, and Monicah Wambugu. Students from the team participated in the AFOG lunch working group meetings.

by Samuel Meyer

Opaque: hard to understand; not clear or lucid; obscure. All algorithms are opaque to those who do not understand how they work, but machine learning can be opaque even to those who built it. As a result, much academic work on explainable machine learning focuses on just trying to explain how the model works to those who built it, let alone opening it up to the general public. Unfortunately, many new machine-learning products sold to governments and large companies are only understandable by their developers. If only the developers understand the systems, how can the public make sure that government systems make fair decisions?

Not all is lost, however. Contrary to the narrative that all machine learning is completely a black box, talking to machine learning practitioners will reveal that some machine learning algorithms are generally considered to be interpretable (such as logistic regression and decision trees). They may not always be as accurate as neural nets or other advanced machine learning, but these algorithms are often used in real-world applications because they are easy to implement and easier for experts to interpret.

As a first step toward making general systems that will let the public look at machine learning, we designed and built a web application that allows arbitrary csv-based datasets be fit with logistic regression and viewed by non-experts. It allows machine-learning developers to share what a model is doing with outsiders. If the non-experts want to add comments about factors or weights in the model, they can.

Also, the web app includes an implementation of equal opportunity, a mathematical definition of fairness created by Moritz Hardt, a member of AFOG. This allows users to see what effect the fairness requirement would have on their dataset.

You can read a full report at our project page or download the code and run it yourself.

Definition of opaque from dictionary.com.

Dr. Blumenstock’s paper, “Estimating Economic Characteristics with Phone Data“, was published in the American Economic Review: Papers and Proceedings

Note: The following is a re-post of AFOG member Shreeharsh Kelkar’s September 25, 2017 post on Scatterplot responding to the controversy over Wang and Kosinski’s (2018) paper about using deep neural networks to recognize “gay” or “straight” faces. At the time of Shreeharsh’s post, Wang and Kosinski’s paper had been accepted for publication but not yet published. The final version of the paper is now published in the Journal of Personality and Social Psychology and can be found here. Shreeharsh argues in this post that at least a part of the opacity of algorithms comes from the ways in which their technical mechanisms and social meanings co-exist side-by-side.

On this blog, and elsewhere, Greggor Mattson, Phil Cohen, and many others have written thoughtful, principled critiques of the recent “gaydar” study by Yilun Wang and Michal Kosinski (henceforth I’ll refer to the authors as just Kosinsky since he seems to be the primary spokesperson).  I fully agree with them: the study both does too much and too little.  It purports to “advance our understanding of the origins of sexual orientation and the limits of human perception” (!) through a paltry analysis of 35,326 images (and responses to these images by anonymous humans on Amazon Mechanical Turk).  And it aims to vaguely warn us about rapacious corporations using machine learning programs to surreptitiously identify sexual orientation but the warning seems almost like an afterthought: if the authors were really serious about this warning, they could have dug deeper with a feasibility study rather than sliding quickly into thinking about the biological underpinnings of sexuality.

As someone who follows and studies the history of artificial intelligence (as I do), there are some striking parallels between the argument between Kosinsky and his critics, and early controversies over AI in the 1960s-80s, and I will also argue, some lessons to be learnt. Early AI was premised on the notion that when human beings did putatively “intelligent” things, they were processing information, a sort of “plan” that was worked out in their heads and then executed.  When philosopher Hubert Dreyfus wrote his famous “Alchemy and Artificial Intelligence” paper for the RAND Corporation in 1965 (later expanded into his book What Computers Can’t Do), he drew on the work of post-foundationalist philosophers like Heidegger, Wittgenstein, and Merleau-Ponty to argue that human action could not be reduced to rule-following or information processing, and once AI systems were taken out of their toy “micro-worlds,” they would fail. For their part, AI researchers argued that critics like Dreyfus moved the “intelligence” goalposts when it suited them. When programs worked (as did the chess and checkers-playing programs in the 1960s and 1970s), the particular tasks they performed were just moved out of the realm of intelligence.

Figure 1: The canon of artificial intelligence. Source: Flickr, Creative Commons License.

One way to understand this debate—the way that participants often talked right past each other—is to understand the different contexts in which the AI researchers and their critics approached what they did.  In what I have found to be one of the best descriptions of what it means to do technical work, Phil Agre, who worked both as an AI researcher and a social scientist, points out that AI researchers rarely care about ideas by themselves.  Rather, an idea is only important if it can be built into a technical mechanism, i.e. if it can be formalized either in mathematics or in machinery.   Agre calls this the “work ethic”:

Computer people believe only what they can build, and this policy imposes a strong intellectual conservatism on the field. Intellectual trends might run in all directions at any speed, but computationalists mistrust anything unless they can nail down all four corners of it; they would, by and large, rather get it precise and wrong than vague and right. They often disagree about how much precision is required, and what kind of precision, but they require ideas that can be assimilated to computational demonstrations that actually get built. This is sometimes called the work ethic: it has to work (p13).

But the “work ethic” is often not something outsiders—and especially outside researchers—get.  To them, the exercise seems intellectually shoddy and perhaps even dangerous.  Here is Agre again:

To get anything nailed down in enough detail to run on a computer requires considerable effort; in particular, it requires that one make all manner of arbitrary commitments on issues that may be tangential to the current focus of theoretical interest. It is no wonder, then, that AI work can seem outrageous to people whose training has instilled different priorities—for example, conceptual coherence, ethnographic adequacy, political relevance, mathematical depth, or experimental support. And indeed it is often totally mysterious to outsiders what canons of progress and good research do govern such a seemingly disheveled enterprise. The answer is that good computational research is an evolving conversation with its own practical reality; a new result gets the pulse of this practical reality by suggesting the outlines of a computational explanation of some aspect of human life. The computationalist’s sense of bumping up against reality itself—of being compelled to some unexpected outcome by the facts of physical readability as they manifest themselves in the lab late at night—is deeply impressive to those who have gotten hold of it. Other details—conceptual, empirical, political, and so forth—can wait. That, at least, is how it feels. [p13, my emphasis].

Figure 2: Courses required to complete a graduate certificate in artificial intelligence. Source: Flickr, Creative Commons License.

This logic of technical work manifests itself even more strangely for something like AI, a field that is about building “intelligent” technical mechanisms, which therefore has to perform a delicate two-step between the “social” and the “technical” domains—but which is nevertheless also a key to its work and its politics.  Agre argues that the work of AI researchers can be described as a series of moves done together, a process that he calls “formalization”: taking a metaphor, often in an intentionalist vocabulary, (e.g. “thinking,” “planning”, “problem-solving,”), attaching some mathematics and machinery to it, and then being able to narrate the working of that machinery in intentional vocabulary.  This process of formalization has a slightly schizophrenic character: the mechanism is precise in its mathematical form and imprecise in its lay form; but being able to move fluidly between the precise and the imprecise is the key to its power. This is not perhaps very different from the contortions that quantitative social science papers perform to hint at causation without really saying it openly (which Dan has called the correlation-causation two-step on this blog).  The struggle in quantitative social science is between a formal definition of causation versus a more narrative one.  AI researchers, of course, perform their two-step with fewer caveats because their goal is to realize their mathematical machinery into actual “working” programs, rather than explain a phenomenon.

To switch abruptly to the present, we can see the same two-step at work in the Kosinsky paper. There is the use of social categories (“gay,” “straight”), the precise reduction of these categories to self-labeled photos with faces, the also-precise realization of a feature-set and standard algorithm to derive the labels for these photos, and then the switch back into narrating the workings of the systems in terms of broader social categories (gender, sexuality, grooming, recognizing).  The oddest thing in the paper is the reference to the “widely accepted prenatal hormone theory (PHT) of sexual orientation” but a closer reading shows that the theory is invoked essentially to provide a “scientific” justification of choices in the design of what is a conventional machine learning classifier.  (My suspicion is that the classifier came first, and the theory came later because of the decision to submit to a psychology journal.  Alternatively, it may have evolved out of the peer review process.)

But if the two-step remains the same, the world of AI today is starkly different.  As I have written before, today’s artificial intelligence is steeped far more in the art of making (real-world) classifications, rather than in the abstract concepts of planning and state-space searching.  Moreover, far from operating in “microworlds” as they did before, contemporary AI programs are all too realizable in the massive infrastructures of Facebook and Google.  (Indeed, one of Dreyfus’ criticisms of early AI was that it would not work in the real world.  No one would argue that today.)   Not surprisingly, the debates over AI have shifted as well: they are much more about questions of bias and discrimination; there’s also far more talk of how “algorithms”—the classifying recipes of the new AI—sometimes seem similar to the discredited sciences of phrenology and physiognomy.

There have been three angles of critique of the Kosinsky study.  The first has been over the researchers’ notion of “informed consent”: as Greggor Mattson points out (see also this Mary Gray post on the old Facebook contagion study), researchers, whether corporate or academic, need to be more cognizant of community norms around anonymity and privacy (especially for marginalized communities) when they scrape what they see as “public” data.   The second has been from quantitative social scientists who find the Kosinksy study lacking by the standards of rigorous social science.  Again, you’ll find no argument from me on that score.  But it bears mentioning that AI researchers are not quantitative social scientists: they are not so much interested in explaining phenomena as they are in building technical systems.  Should quantitative social scientists take the logic of technical work into account when they criticize the big claims of the Kosinksy study?  Maybe so, maybe not; there are certainly grounds to think that the dialogue between quantitative social scientists (accustomed to the correlation-causation two-step) and AI classifier-builders will be productive, given that the use of correlations is now emerging as central to both fields.

My own angle on the study is from the third  perspective, that of interpretive social science. When we social scientists find the use of social categories in the Kosinsky study dubious (and even outright wrong), we are reacting to what we see as the irresponsible use of a socially meaningful vocabulary to describe the working of an arcane technical mechanism.  On this score though, the history of the older debates over AI is worrying.  If my reading of the history of AI is right (I’m open to other interpretations), those debates went nowhere because people were talking past each other.  Much ink was spilled, feuds were born, but everything went right on as it did before: AI was still AI, the social sciences were still the social sciences, and the differences remained stark and deep. (Indeed, the work of people like Agre and Lucy Suchman got taken up more in the computer science sub-field of human-computer interaction (HCI) than in AI proper.)

Could we do better this time?  I don’t know.  I might start by asking the AI researchers to be careful with their use of metaphors and socially meaningful categories.  As the AI researcher Drew McDermott put it in his marvelously titled “Artificial Intelligence Meets Natural Stupidity” article written in the 1970s, some of the feuds over early AI really could have been avoided if the AI researchers had used more technical names for their systems rather than “wishful mnemonics.”

Many instructive examples of wishful mnemonics by AI researchers come to mind once you see the point.  Remember GPS? (Ernest and Newell 1969).  By now, “GPS” is a colorless term denoting a particularly stupid program to solve puzzles.  But it originally meant “General Problem Solver,” which caused everybody a lot of needless excitement and distraction.  It should have been called LFGNS–“Local-Feature-Guided Network Searcher.”

For our part, we may want to collaborate with AI researchers to think about social categories relationally and historically rather than through an essentialist lens.  But successful collaborations require care and at least a sense of the other culture.  First, we may want to keep in mind through our collaborations that there is an inner logic to technical work.  To put it in Agre’s terms, technical work evolves in conversation with its own practical reality and does not necessarily aim at conceptual coherence.  Second, when they do draw on the social sciences, AI researchers tend to look at psychology and economics (and philosophy), rather than, say, sociology, history or anthropology.  (And not surprisingly, it is also in psychology and economics that machine learning has been taken up enthusiastically.  Kosinsky, for instance, has a PhD in psychology but seems to describe himself as a “data scientist.”)  This is not a coincidence: computer science, psychology and economics were all transformed by the cognitive revolution and took up, in various ways, the idea of information processing that was central to that revolution.  They are, all of them, in Philip Mirowski’s words, “cyborg sciences” and as such, concepts can travel easier between them.  So interpretive social scientists have their work cut out.  But even if our effort is doomed to fail, it should be our responsibility to open a dialogue with AI researchers and push for what we might call a non-essentialist understanding of social categories into AI.

Seeing Through the Fog

March 19th, 2018

Welcome to the AFOG Blog! We will use this space to post what we hope are accessible and provocative think pieces and reactions to academic research and news stories. Posts about what? Allow us to use this initial blog post to answer that question and introduce ourselves.

Algorithms and computational tools/systems, particularly as applied to artificial intelligence and machine learning, are increasingly being used by firms and governments in domains of socially consequential classification and decision-making. But their construction, application, and consequences are raising new concerns over issues of fairness, bias, transparency, interpretability, and accountability. The development of approaches or solutions to address these challenges are still nascent. And they require attention from more than just technologists and engineers, as they are playing out in domains of longstanding interest to social scientists and scholars of media, law, and policy, including social equality, civil rights, labor and automation, and the evolution of the news media.

In the fall of 2017, Professors Jenna Burrell and Deirdre Mulligan at the UC Berkeley School of Information began the Algorithmic Fairness and Opacity Group (AFOG), a working group that brings together UC Berkeley faculty, postdocs, and graduate students to develop new ideas, research directions, and policy recommendations around these topics. We take an interdisciplinary approach to our research, with members based at a variety of schools and departments across campus. These include UC Berkeley’s School of Information, Boalt Hall School of Law, Haas School of Business, the Goldman School of Public Policy, the departments of Electrical Engineering and Computer Sciences (EECS) and Sociology, the Berkeley Institute of Data Science (BIDS), the Center for Science, Technology, Medicine & Society (CSTMS), and the Center for Technology, Society & Policy (CTSP).

We meet roughly biweekly at the School of Information for informal discussions, presentations, and workshops. We also host a speaker series that brings experts from academia and the technology industry to campus to give public talks and take part in interdisciplinary conversations. AFOG is supported by UC Berkeley’s School of Information and a grant from Google Trust and Safety.

Below is a sampling of some of the questions that we seek to address:

  • How do trends in data-collection and algorithmic classification relate to the restructuring of life chances, opportunities, and ultimately the social mobility of individuals and groups in society?
  • How does an algorithmically informed mass media and social media shape the stability of our democracy?
  • How can we design user interfaces for machine-learning systems that will support user understanding, empowered decision-making, and human autonomy?
  • What tools and techniques are emerging that offer ways to mitigate transparency and/or fairness problems?
  • Which methods are best suited to particular domains of application?
  • How can we identify and transcend differences across disciplines in order to make progress on issues of algorithmic opacity and fairness?

Look for more from us on the AFOG Blog in the weeks and months to come!