Replication data for: Why Are There Still So Many Jobs? The History and Future of Workplace Automation

A recent normative turn in computer science has brought concerns about fairness, bias, and accountability to the core of the field. Yet recent scholarship has warned that much of this technical work treats problematic features of the status quo as fixed, and fails to address deeper patterns of injustice and inequality. While acknowledging these critiques, we posit that computational research has valuable roles to play in addressing social problems — roles whose value can be recognized even from a perspective that aspires toward fundamental social change. In this paper, we articulate four such roles, through an analysis that considers the opportunities as well as the significant risks inherent in such work. Computing research can serve as a diagnostic, helping us to understand and measure social problems with precision and clarity. As a formalizer, computing shapes how social problems are explicitly defined — changing how those problems, and possible responses to them, are understood. Computing serves as rebuttal when it illuminates the boundaries of what is possible through technical means. And computing acts as synecdoche when it makes long-standing social problems newly salient in the public eye. We offer these paths forward as modalities that leverage the particular strengths of computational work in the service of social change, without overclaiming computing’s capacity to solve social problems on its own.

pdf/roles-for-computing-in-social-change-y0u5dw3idu.pdf

Roles for Computing in Social Change

Evaluating bias, fairness, and social impact in monolingual language models is a difficult task. This challenge is further compounded when language modeling occurs in a multilingual context. Considering the implication of evaluation biases for large multilingual language models, we situate the discussion of bias evaluation within a wider context of social scientific research with computational work.We highlight three dimensions of developing multilingual bias evaluation frameworks: (1) increasing transparency through documentation, (2) expanding targets of bias beyond gender, and (3) addressing cultural differences that exist between languages.We further discuss the power dynamics and consequences of training large language models and recommend that researchers remain cognizant of the ramifications of developing such technologies.

/pdf/you-reap-what-you-sow-on-the-challenges-of-bias-evaluation-27mw6sqw.pdf

You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings

In recent years, numerous public, private, and non-governmental organizations (NGOs) have produced documents addressing the ethical implications of artificial intelligence (AI) These normative documents include principles, frameworks, and policy strategies that articulate the ethical concerns, priorities, and associated strategies of leading organizations and governments around the world We examined 112 such documents from 25 countries that were produced between 2016 and the middle of 2019 While other studies identified some degree of consensus in such documents, our work highlights meaningful differences across public, private, and NGO sectors We analyzed each document in terms of how many of 25 ethical topics were covered and the depth of discussion for those topics As compared to documents from private entities, NGO and public sector documents reflect more ethical breadth in the number of topics covered, are more engaged with law and regulation, and are generated through processes that are more participatory These findings may reveal differences in underlying beliefs about an organization’s responsibilities, the relative importance of relying on experts versus including representatives from the public, and the tension between prosocial and economic goals

AI Ethics in the Public, Private, and NGO Sectors: A Review of a Global Document Collection

Numerous toolkits have been developed to support ethical AI development. However, toolkits, like all tools, encode assumptions in their design about what work should be done and how. In this paper, we conduct a qualitative analysis of 27 AI ethics toolkits to critically examine how the work of ethics is imagined and how it is supported by these toolkits. Specifically, we examine the discourses toolkits rely on when talking about ethical issues, who they imagine should do the work of ethics, and how they envision the work practices involved in addressing ethics. Among the toolkits, we identify a mismatch between the imagined work of ethics and the support the toolkits provide for doing that work. In particular, we identify a lack of guidance around how to navigate labor, organizational, and institutional power dynamics as they relate to performing ethical work. We use these omissions to chart future work for researchers and designers of AI ethics toolkits.

/pdf/seeing-like-a-toolkit-how-toolkits-envision-the-work-of-ai-6l55bx2l.pdf

Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics

Companies have considered adoption of various high-level artificial intelligence (AI) principles for responsible AI, but there is less clarity on how to implement these principles as organizational practices. This paper reviews the principles-to-practices gap. We outline five explanations for this gap ranging from a disciplinary divide to an overabundance of tools. In turn, we argue that an impact assessment framework which is broad, operationalizable, flexible, iterative, guided, and participatory is a promising approach to close the principles-to-practices gap. Finally, to help practitioners with applying these recommendations, we review a case study of AI's use in forest ecosystem restoration, demonstrating how an impact assessment framework can translate into effective and responsible AI practices.

Principles to Practices for Responsible AI: Closing the Gap.

As artificial intelligence (AI) permeates across social and economic life, its ethical and governance implications have come to the forefront. Active debates surround AI’s role in labor displacement, autonomous vehicles, military, misinformation, healthcare, education, and more. As societies collectively grapple with these challenges, new opportunities for AI to proactively contribute to social good (AI4SG) and equity (AI4Eq) have also been proposed [1] , [2] , such as Microsoft’s AI for Earth program. These efforts highlight the potential of AI to address global challenges and help achieve targets like the United Nation’s sustainable development goals (SDGs) [3] . Yet, whether AI efforts are directed explicitly at social good and equity or not, there are many barriers that stand between aspirations to be responsible and the translation of these aspirations into concrete practicalities.

Explaining the Principles to Practices Gap in AI

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Principles to Practices for Responsible AI: Closing the Gap.

Explaining the Principles to Practices Gap in AI