The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the "social good", of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes.

In any normal year, Cambridge Analytica would have been the biggest story. Facebook alone had a royal flush of scandals, including a huge data breach in September, becoming the subject of multiple class action lawsuits for discrimination, accusations of inciting ethnic cleansing in Myanmar, potential violations of the Fair Housing Act, and hosting masses of fake Russian accounts. Throughout the year, Facebook executives were frequently summoned to testify, with Mark Zuckerberg himself facing the US Senate in April and the European Parliament in May. News broke in March that Google was building AI systems for the Department of Defense's drone surveillance program, Project Maven. The news kicked off an unprecedented wave of tech worker organizing and dissent.

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of "AI for Social Good", more specifically "Data Science for Social Good". Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: "attacks" as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.

The UK's first major Parliamentary inquiry into Artificial Intelligence has called for a new cross-sector ethics code to ensure that the country becomes a world leader in AI. Lord Clement-Jones, the Chairman of The House of Lords Select Committee on Artificial Intelligence, told Techworld that an ethical approach was essential to ensure public support for AI. "What we want is to make sure that the public is fully trusting in this technology, and you can only do that if they believe it's for the benefit of them and others when they're being applied, and also that it's transparent and unbiased in its application," he said. The proposed "AI Code" could attract public support by creating consistent guidelines for developing and using AI across all organisations and companies in both the public and private sectors. In a report titled AI in the UK: Ready, Willing and Able?, the committee set out five principles to form the basis of the code, which could be adopted internationally: This AI code could provide the basis for future statutory regulation, but the committee stopped short of recommending new regulation specifically for AI at this point.

The tech industry is having a moment of reflection. Even Mark Zuckerberg and Tim Cook are talking openly about the downsides of software and algorithms mediating our lives. And while calls for regulation have been met with increased lobbying to block or shape any rules, some people around the industry are entertaining forms of self regulation. Microsoft released a 151-page book last month on the effects of artificial intelligence on society that argued "it could make sense" to bind coders to a pledge like that taken by physicians to "first do no harm." In San Francisco Tuesday, dozens of data scientists from tech companies, governments, and nonprofits gathered to start drafting an ethics code for their profession.