Archive for August, 2018

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:

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.

On Thursday, October 4th at 5:30pm the Center for Technology, Society & Policy (CTSP) and the School of Information’s Information Management Student Association (IMSA) are co-hosting their third annual Social Impact Un-Pitch Day!

Join CTSP and IMSA to brainstorm ideas for projects that address the challenges of technology, society, and policy. We welcome students, community organizations, local municipal partners, faculty, and campus initiatives to discuss discrete problems that project teams can take on over the course of this academic year. Teams will be encouraged to apply to CTSP to fund their projects.

Location: Room 202, in South Hall.

RSVP here!


  • 5:40 Introductions from IMSA and CTSP
  • 5:45 Example Projects
  • 5:50 Sharing Un-Pitches

We’ve increased the time for Un-Pitches! (Still 3-minutes per Un-Pitch)

  • 6:40 Mixer (with snacks and refreshments)



Un-Pitches are meant to be informal and brief introductions of yourself, your idea, or your organization’s problem situation. Un-pitches can include designing technology, research, policy recommendations, and more. Students and social impact representatives will be given 3 minutes to present their Un-Pitch. In order to un-pitch, please share 1-3 slides, as PDF and/or a less than 500-word description—at this email: ude.yelekrebnull@pstc. You can share slides and/or description of your ideas even if you aren’t able to attend. Deadline to share materials: midnight October 1st, 2018.

Funding Opportunities

The next application round for fellows will open in November. CTSP’s fellowship program will provide small grants to individuals and small teams of fellows for 2019. CTSP also has a recurring offer of small project support.

Prior Projects & Collaborations

Here are several examples of projects that members of the I School community have pursued as MIMS final projects or CTSP Fellow projects (see more projects from 2016, 2017, and 2018).


Skills & Interests of Students

The above projects demonstrate a range of interests and skills of the I School community. Students here and more broadly on the UC Berkeley campus are interested and skilled in all aspects of where information and technology meets people—from design and data science, to user research and information policy.

RSVP here!

Location: South Hall Rm 202

Time: 5:30-7pm (followed by light refreshments)

CTSP’s first event of the semester!

Co-Sponsored with the Center for Long-Term Cybersecurity

Please join us for a panel discussion featuring award-winning tech reporter Cyrus Farivar, whose new book, Habeas Data, explores how the explosive growth of surveillance technology has outpaced our understanding of the ethics, mores, and laws of privacy. Habeas Data explores ten historic court decisions that defined our privacy rights and matches them against the capabilities of modern technology. Mitch Kapor, co-founder, Electronic Frontier Foundation, said the book was “Essential reading for anyone concerned with how technology has overrun privacy.”

The panel will be moderated by 2017 and 2018 CTSP Fellow Steve Trush, a MIMS 2018 graduate and now a Research Fellow at the Center for Long-Term Cybersecurity (CLTC). He was on a CTSP project starting in 2017 that provided a report to the Oakland Privacy Advisory Commission—read an East Bay Express write-up on their work here.

The panelists will discuss what public governance models can help local governments protect the privacy of citizens—and what role citizen technologists can play in shaping these models. The discussion will showcase the ongoing collaboration between the UC Berkeley School of Information and the Oakland Privacy Advisory Commission (OPAC). Attendees will learn how they can get involved in addressing issues of governance, privacy, fairness, and justice related to state surveillance.


  • Cyrus Farivar, Author, Habeas Data: Privacy vs. the Rise of Surveillance Tech
  • Deirdre Mulligan, Associate Professor in the School of Information at UC Berkeley, Faculty Director, UC Berkeley Center for Law & Technology
  • Catherine Crump, Assistant Clinical Professor of Law, UC Berkeley; Director, Samuelson Law, Technology & Public Policy Clinic.
  • Camille Ochoa, Coordinator, Grassroots Advocacy; Electronic Frontier Foundation
  • Moderated by Steve Trush, Research Fellow, UC Berkeley Center for Long-Term Cybersecurity

The panel will be followed by a reception with light refreshments. Building is wheelchair accessible – wheelchair users can enter through the ground floor level and take the elevator to the second floor.

This event will not be taped or live-streamed.

RSVP here to attend.


Panelist Bios:

Cyrus [“suh-ROOS”] Farivar is a Senior Tech Policy Reporter at Ars Technica, and is also an author and radio producer. His second book, Habeas Data, about the legal cases over the last 50 years that have had an outsized impact on surveillance and privacy law in America, is out now from Melville House. His first book, The Internet of Elsewhere—about the history and effects of the Internet on different countries around the world, including Senegal, Iran, Estonia and South Korea—was published in April 2011. He previously was the Sci-Tech Editor, and host of “Spectrum” at Deutsche Welle English, Germany’s international broadcaster. He has also reported for the Canadian Broadcasting Corporation, National Public Radio, Public Radio International, The Economist, Wired, The New York Times and many others. His PGP key and other secure channels are available here.

Deirdre K. Mulligan is an Associate Professor in the School of Information at UC Berkeley, a faculty Director of the Berkeley Center for Law & Technology, and an affiliated faculty on the Center for Long-Term Cybersecurity.  Mulligan’s research explores legal and technical means of protecting values such as privacy, freedom of expression, and fairness in emerging technical systems.  Her book, Privacy on the Ground: Driving Corporate Behavior in the United States and Europe, a study of privacy practices in large corporations in five countries, conducted with UC Berkeley Law Prof. Kenneth Bamberger was recently published by MIT Press. Mulligan and  Bamberger received the 2016 International Association of Privacy Professionals Leadership Award for their research contributions to the field of privacy protection.

Catherine Crump: Catherine Crump is an Assistant Clinical Professor of Law and Director of the Samuelson Law, Technology & Public Policy Clinic. An experienced litigator specializing in constitutional matters, she has represented a broad range of clients seeking to vindicate their First and Fourth Amendment rights. She also has extensive experience litigating to compel the disclosure of government records under the Freedom of Information Act. Professor Crump’s primary interest is the impact of new technologies on civil liberties. Representative matters include serving as counsel in the ACLU’s challenge to the National Security Agency’s mass collection of Americans’ call records; representing artists, media outlets and others challenging a federal internet censorship law, and representing a variety of clients seeking to invalidate the government’s policy of conducting suspicionless searches of laptops and other electronic devices at the international border.

Prior to coming to Berkeley, Professor Crump served as a staff attorney at the ACLU for nearly nine years. Before that, she was a law clerk for Judge M. Margaret McKeown at the United States Court of Appeals for the Ninth Circuit.

Camille Ochoa: Camille promotes the Electronic Frontier Foundation’s grassroots advocacy initiative (the Electronic Frontier Alliance) and coordinates outreach to student groups, community groups, and hacker spaces throughout the country. She has very strong opinions about food deserts, the school-to-prison pipeline, educational apartheid in America, the takeover of our food system by chemical companies, the general takeover of everything in American life by large conglomerates, and the right to not be spied on by governments or corporations.

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.


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).