How can we engage the ethics of data science in practice? barocas & boyd

Stereotypical white male figure of a data scientist

From “Engaging the ethics of data science in practice” published in Communications of the ACM, available here.

The critical writing on data science has taken the paradoxical position of insisting that normative issues pervade all work with data while leaving unaddressed the issue of data scientists’ ethical agency. Critics need to consider how data scientists learn to think about and handle these trade-offs, while practicing data scientists need to be more forthcoming about all of the small choices that shape their decisions and systems.

Technical actors are often far more sophisticated than critics at understanding the limits of their analysis. In many ways, the work of data scientists is a qualitative practice: they are called upon to parse an amorphous problem, wrangle a messy collection of data, and make it amenable to systematic analysis. To do this work well, they must constantly struggle to understand the contours and the limitations of both the data and their analysis. Practitioners want their analysis to be accurate and they are deeply troubled by the limits of tests of validity, the problems with reproducibility, and the shortcomings of their methods.

Many data scientists are also deeply disturbed by those who are coming into the field without rigorous training and those who are playing into the hype by promising analyses that are not technically or socially responsible. In this way, they should serve as allies with critics. Both see a need for nuances within the field. Unfortunately, universalizing critiques may undermine critics’ opportunities to work with data scientists to address meaningfully some of the most urgent problems.

Reblog> Good Data: Call for Proposals for Theory on Demand edited book

My Cayla Doll

From the Institute of Network Cultures:

Good Data: Call for Proposals for an INC Theory on Demand edited book

Editors: Angela Daly (Queensland University of Technology), Kate Devitt (Queensland University of Technology) & Monique Mann (Queensland University of Technology).

In recent years, there has been an exponential increase in the collection, aggregation and automated analysis of information by government and private actors, and in response to this there has been a significant critique regarding what could be termed ‘bad’ data practices in the globalised digital economy. These include the mass gathering of data about individuals, in opaque, unethical and at times illegal ways, and the increased use of that data in unaccountable and potentially discriminatory forms of algorithmic decision-making by both state agencies and private companies. Issues of data ethics and data justice are only likely to increase in importance given the totalizing datafication of society and the introduction of new technologies such as artificial intelligence and automation.

In order to paint an alternative, more optimistic but still pragmatic picture of the datafied future, this open access edited collection will examine and propose what could be termed ‘good’ and ‘ethical’ data practices, underpinned by values and principles such as (but not limited to):

  • privacy/regulation/information security by design
  • due process rights
  • procedural legitimacy
  • the protection of individual and collective autonomy
  • digital sovereignty
  • digital anti-discrimination
  • data and intersectionality
  • ethical labour practices
  • environmental sustainability.

Chapters should be short contributions (2500-5000 words) which can take differing forms, for example:

  • Manifestos for Good Data
  • Position papers
  • Traditional academic chapters

Chapters can be theoretical takes or provocations on what Good Data is or should be, or can be case studies of particular Good Data projects and initiatives e.g. Indigenous data sovereignty initiatives, data cooperatives etc. Chapters can also be critiques of initiatives/movements which claim to be ethical but in fact fall short. All chapters, including academic ones, should be written in an accessible way and avoid the excessive use of jargon, etc. Academic chapters will be peer-reviewed. Other contributions will be editor-reviewed.

We encourage contributions from throughout the world and from different disciplinary perspectives: philosophy, media and communications, cultural studies, STS, law, criminology, information systems, computer science etc.

Proposals for chapters (up to 250 words) should be sent to Kayleigh Hodgkinson Murphy (kayleigh.murphy@qut.edu.au) by Friday 15 December 2017. Please include a brief biography (academic/practitioner) and signal what kind of chapter you are proposing (manifesto/academic chapter, etc).

If you have an idea for a chapter and want to discuss it before submitting a proposal, please contact Angela Daly (angela.daly@qut.edu.au) as soon as possible. We may be able to pair, for example, practitioners with academic authors on request.

Decisions on proposals will be made by mid-January 2017, with a first full draft of chapters to be submitted by 31 March 2018. We anticipate the book will be finalized and launched in late 2018, as part of the Institute of Network Cultures’ Theory on Demand series.

Great opportunity > Internship with the Social Media Collective (Microsoft)

Twitter

Via Nancy Baym:

Call for applications! 2018 summer internship, MSR Social Media Collective

APPLICATION DEADLINE: JANUARY 19, 2018

Microsoft Research New England (MSRNE) is looking for advanced PhD students to join the Social Media Collective (SMC) for its 12-week Internship program. The Social Media Collective (in New England, we are Nancy Baym, Tarleton Gillespie, and Mary Gray, with current postdocs Dan Greene and Dylan Mulvin) bring together empirical and critical perspectives to understand the political and cultural dynamics that underpin social media technologies. Learn more about us here.

MSRNE internships are 12-week paid stays in our lab in Cambridge, Massachusetts. During their stay, SMC interns are expected to devise and execute their own research project, distinct from the focus of their dissertation (see the project requirements below). The expected outcome is a draft of a publishable scholarly paper for an academic journal or conference of the intern’s choosing. Our goal is to help the intern advance their own career; interns are strongly encouraged to work towards a creative outcome that will help them on the academic job market.

The ideal candidate may be trained in any number of disciplines (including anthropology, communication, information studies, media studies, sociology, science and technology studies, or a related field), but should have a strong social scientific or humanistic methodological, analytical, and theoretical foundation, be interested in questions related to media or communication technologies and society or culture, and be interested in working in a highly interdisciplinary environment that includes computer scientists, mathematicians, and economists.

Primary mentors for this year will be Nancy Baym and Tarleton Gillespie, with additional guidance offered by other members of the SMC. We are looking for applicants working in one or more of the following areas:

  1. Personal relationships and digital media
  2. Audiences and the shifting landscapes of producer/consumer relations
  3. Affective, immaterial, and other frameworks for understanding digital labor
  4. How platforms, through their design and policies, shape public discourse
  5. The politics of algorithms, metrics, and big data for a computational culture
  6. The interactional dynamics, cultural understanding, or public impact of AI chatbots or intelligent agents

Interns are also expected to give short presentations on their project, contribute to the SMC blog, attend the weekly lab colloquia, and contribute to the life of the community through weekly lunches with fellow PhD interns and the broader lab community. There are also natural opportunities for collaboration with SMC researchers and visitors, and with others currently working at MSRNE, including computer scientists, economists, and mathematicians. PhD interns are expected to be on-site for the duration of their internship.

Applicants must have advanced to candidacy in their PhD program by the time they start their internship. (Unfortunately, there are no opportunities for Master’s students or early PhD students at this time). Applicants from historically marginalized communities, underrepresented in higher education, and students from universities outside of the United States are encouraged to apply.

PEOPLE AT MSRNE SOCIAL MEDIA COLLECTIVE

The Social Media Collective is comprised of full-time researchers, postdocs, visiting faculty, Ph.D. interns, and research assistants. Current projects in New England include:

  • How does the use of social media affect relationships between artists and audiences in creative industries, and what does that tell us about the future of work? (Nancy Baym)
  • How are social media platforms, through their algorithmic design and user policies, taking up the role of custodians of public discourse? (Tarleton Gillespie)
  • What are the cultural, political, and economic implications of crowdsourcing as a new form of semi-automated, globally-distributed digital labor? (Mary L. Gray)
  • How do public institutions like schools and libraries prepare workers for the information economy, and how are they changed in the process? (Dan Greene)
  • How are media standards made, and what do their histories tell us about the kinds of things we can represent? (Dylan Mulvin)

SMC PhD interns may also have the opportunity to connect with our sister Social Media Collective members in New York City. Related projects in New York City include:

  • What are the politics, ethics, and policy implications of artificial intelligence and data science? (Kate Crawford, MSR-NYC)
  • What are the social and cultural issues arising from data-centric technological development? (danah boyd, Data & Society Research Institute)

For more information about the Social Media Collective, and a list of past interns, visit the About page of our blog. For a complete list of all permanent researchers and current postdocs based at the New England lab, see: http://research.microsoft.com/en-us/labs/newengland/people/bios.aspx

Read more.

‘Pax Technica’ Talking Politics, Naughton & Howard

Nest - artwork by Jakub Geltner

This episode of the ‘Talking Politics‘ podcast is a conversation between LRB journalist John Naughton and the Oxford Internet Institute’s Professor Phillip Howard ranging over a number of issues but largely circling around the political issues that may emerge from ‘Internets of Things’ (the plural is important in the argument) that are discussed in Howard’s book ‘Pax Technica‘. Worth a listen if you have time…

One of the slightly throw away bits of the conversation, which didn’t concern the tech, that interested me was when Howard comments on the kind of book Pax Technica is – a ‘popular’ rather than ‘scholarly’ book and how that had led to a sense of dismissal by some. It seems nuts (to me, anyway) when we’re all supposed to be engaging in ‘impact’, ‘knowledge exchange’ and so on that opting to write a £17 paperback that opens out debate, instead of a £80+ ‘scholarly’ hardback, is frowned upon. I mean I understand some of the reasons why but still…

UK Government ramping up ‘robotic process automation’

Still from George Lucas' THX1138

Quite by chance I stumbled across the twitter coverage of a UK Authority event entitled “Return of the Bots” yesterday. There were a range of speakers it seems, from public and private sectors. An interesting element was the snippets about the increasing use of process automation by the UK Government.

Here’s some of the tweets I ‘favourited’ for further investigation (below). I hadn’t quite appreciated where government had got to. It would be interesting to look into the rationale both central and local government are using for RPA – I assume it is cost-driven(?). I hope to follow up some of  this…

Reblog> (video): Gillian Rose – Tweeting the Smart City

Smart City visualisation

Via The Programmable City.

Seminar 2 (video): Gillian Rose – Tweeting the Smart City

We are delighted to share the video of our second seminar in our 2017/18 series, entitled Tweeting the Smart City: The Affective Enactments of the Smart City on Social Media given by Professor Gillian Rose from Oxford University on the 26th October 2017 and co-hosted with the Geography Department at Maynooth University.Abstract
Digital technologies of various kinds are now the means through which many cities are made visible and their spatialities negotiated. From casual snaps shared on Instagram to elaborate photo-realistic visualisations, digital technologies for making, distributing and viewing cities are more and more pervasive. This talk will explore some of the implications of that digital mediation of urban spaces. What forms of urban life are being made visible in these digitally mediated cities, and how? Through what configurations of temporality, spatiality and embodiment? And how should that picturing be theorised? Drawing on recent work on the visualisation of so-called ‘smart cities’ on social media, the lecture will suggest the scale and pervasiveness of digital imagery now means that notions of ‘representation’ have to be rethought. Cities and their inhabitants are increasingly mediated through a febrile cloud of streaming image files; as well as representing cities, this cloud also operationalises particular, affective ways of being urban. The lecture will explore some of the implications of this shift for both theory and method as well as critique.

Reblog> New paper: A smart place to work? Big data systems, labour, control, and modern retail stores

Gilbreth motion studies light painting

From the Programmable City team, looks interesting:

New paper: A smart place to work? Big data systems, labour, control, and modern retail stores

The modern retail store is a complex coded assemblage and data-intensive environment, its operations and management mediated by a number of interlinked big data systems. This paper draws on an ethnography of a superstore in Ireland to examine how these systems modulate the functioning of the store and working practices of employees. It was found that retail work involves a continual movement between a governance regime of control reliant on big data systems which seek to regulate and harnesses formal labour and automation into enterprise planning, and a disciplinary regime that deals with the symbolic, interactive labour that workers perform and acts as a reserve mode of governmentality if control fails. This continual movement is caused by new systems of control being open to vertical and horizontal fissures. While retail functions as a coded assemblage of control, systems are too brittle to sustain the code/space and governmentality desired.

Access the PDF here

The Economist ‘Babbage’ podcast: “Deus Ex Machina”

Glitched still from the film "Her"

An interesting general (non-academic, non-technical) discussion about what “AI” is, what it means culturally and how it is variously thought about. Interesting to reflect on the way ideas about computation, “algorithms”, “intelligence” and so on play out… something that maybe isn’t discussed enough… I like the way the discussion turns around “thinking” and the suggestion of the word “reckoning”. Worth a listen…

AI Now report

My Cayla Doll

The AI Now Institute have published their second annual report with plenty of interesting things in it. I won’t try and summarise it or offer any analysis (yet). It’s worth a read:

The AI Now Institute, an interdisciplinary research center based at New York University, announced today the publication of its second annual research report. In advance of AI Now’s official launch in November, the 2017 report surveys the current use of AI across core domains, along with the challenges that the rapid introduction of these technologies are presenting. It also provides a set of ten core recommendations to guide future research and accountability mechanisms. The report focuses on key impact areas, including labor and automation, bias and inclusion, rights and liberties, and ethics and governance.

“The field of artificial intelligence is developing rapidly, and promises to help address some of the biggest challenges we face as a society,” said Kate Crawford, cofounder of AI Now and one of the lead authors of the report. “But the reason we founded the AI Now Institute is that we urgently need more research into the real-world implications of the adoption of AI and related technologies in our most sensitive social institutions. People are already being affected by these systems, be it while at school, looking for a job, reading news online, or interacting with the courts. With this report, we’re taking stock of the progress so far and the biggest emerging challenges that can guide our future research on the social implications of AI.”

There’s also a sort of Exec. Summary, a list of “10 Top Recommendations for the AI Field in 2017” on Medium too. Here’s the short version of that:

  1. 1. Core public agencies, such as those responsible for criminal justice, healthcare, welfare, and education (e.g “high stakes” domains) should no longer use ‘black box’ AI and algorithmic systems.
  2. 2. Before releasing an AI system, companies should run rigorous pre-release trials to ensure that they will not amplify biases and errors due to any issues with the training data, algorithms, or other elements of system design.
  3. 3. After releasing an AI system, companies should continue to monitor its use across different contexts and communities.
  4. 4. More research and policy making is needed on the use of AI systems in workplace management and monitoring, including hiring and HR.
  5. 5. Develop standards to track the provenance, development, and use of training datasets throughout their life cycle.
  6. 6. Expand AI bias research and mitigation strategies beyond a narrowly technical approach.
  7. 7. Strong standards for auditing and understanding the use of AI systems “in the wild” are urgently needed.
  8. 8. Companies, universities, conferences and other stakeholders in the AI field should release data on the participation of women, minorities and other marginalized groups within AI research and development.
  9. 9. The AI industry should hire experts from disciplines beyond computer science and engineering and ensure they have decision making power.
  10. 10. Ethical codes meant to steer the AI field should be accompanied by strong oversight and accountability mechanisms.

Which sort of reads, to me, as: “There should be more social scientists involved” 🙂