Responsible AI Systems: Who are the Stakeholders?

Deshpande, Advait; Sharp, Helen

doi:10.1145/3514094.3534187

Cited by 29 publications

(15 citation statements)

References 44 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In hybridity, there is a bidirectional impact where business models are transported into the public sphere and public issues are transferred to business goals [138]. Although there are some ideas to formulate PPPs and make use of hybridity on the fronts of robotics [121] and AI [12,162], most ventures and theorizing has focused on having the main responsibility in public or in private hands [3,20,31,150]. Hence, previous solutions mostly take the form of proposing strong governmental control over AI regulation, or delegating responsibility to the industry.…”

Section: Benefits and Problems With Previous Solutionsmentioning

confidence: 99%

Managing the race to the moon: Global policy and governance in Artificial Intelligence regulation—A contemporary overview and an analysis of socioeconomic consequences

Walter

2024

Discov Artif Intell

View full text Add to dashboard Cite

This paper delves into the complexities of global AI regulation and governance, emphasizing the socio-economic repercussions of rapid AI development. It scrutinizes the challenges in creating effective governance structures amidst the AI race, considering diverse global perspectives and policies. The discourse moves beyond specific corporate examples, addressing broader implications and sector-wide impacts of AI on employment, truth discernment, and democratic stability. The analysis focuses on contrasting regulatory approaches across key regions—the United States, European Union, Asia, Africa, and the Americas and thus highlighting the variations and commonalities in strategies and implementations. This comparative study reveals the intricacies and hurdles in formulating a cohesive global policy for AI regulation. Central to the paper is the examination of the dynamic between rapid AI innovation and the slower pace of regulatory and ethical standard-setting. It critically evaluates the advantages and drawbacks of shifting regulatory responsibilities between government bodies and the private sector. In response to these challenges, the discussion proposes an innovative and integrated regulatory model. The model advocates for a collaborative network that blends governmental authority with industry expertise, aiming to establish adaptive, responsive regulations (called “dynamic laws”) that can evolve with technological advancements. The novel approach aims to bridge the gap between rapid AI advancements in the industry and the essential democratic processes of law-making.

show abstract

Section: Benefits and Problems With Previous Solutionsmentioning

confidence: 99%

Managing the race to the moon: Global policy and governance in Artificial Intelligence regulation—A contemporary overview and an analysis of socioeconomic consequences

Walter

2024

Discov Artif Intell

View full text Add to dashboard Cite

show abstract

“…Third, the idea of fairness is also subjective, especially when diverse stakeholders and views are involved (ibid.). AI brings together tech companies, partners, investors (private and institutional), customers and/or end-users, community members, researchers, regulators, and policymakers, to name a few (Dehspande and Sharp 2022), and their views, needs and goals arguably collide rather than align with one another. Fourth, language constantly changes and evolves (Ferrera 2023), which means that some biases might never be fully addressed or indeed removed from AI-future research will be able to tell us more.…”

Section: Social Changementioning

confidence: 99%

“…Undoing gender in and through generative AI requires coordination and oversight. When it comes to accountability and governance, time and effort need to be invested to identify key stakeholders and work with them and build technological ecosystems that benefit everyone equally (Bender et al 2021;Dehspande and Sharp 2022). However, as we know, AI technologies are already on the market-with performative effects when it comes to gender justice and inequality issues.…”

Section: Next Steps: Planning Implementation Accountability and Overs...mentioning

confidence: 99%

What ChatGPT Tells Us about Gender: A Cautionary Tale about Performativity and Gender Biases in AI

Gross

2023

Social Sciences

View full text Add to dashboard Cite

Large language models and generative AI, such as ChatGPT, have gained influence over people’s personal lives and work since their launch, and are expected to scale even further. While the promises of generative artificial intelligence are compelling, this technology harbors significant biases, including those related to gender. Gender biases create patterns of behavior and stereotypes that put women, men and gender-diverse people at a disadvantage. Gender inequalities and injustices affect society as a whole. As a social practice, gendering is achieved through the repeated citation of rituals, expectations and norms. Shared understandings are often captured in scripts, including those emerging in and from generative AI, which means that gendered views and gender biases get grafted back into social, political and economic life. This paper’s central argument is that large language models work performatively, which means that they perpetuate and perhaps even amplify old and non-inclusive understandings of gender. Examples from ChatGPT are used here to illustrate some gender biases in AI. However, this paper also puts forward that AI can work to mitigate biases and act to ‘undo gender’.

show abstract

“…There is the danger that technological engineers, software developers and businesspeople do not necessarily have the same goals and incentives as individual consumers, ordinary citizens, policy makers, or societies at large. As such, given the rapid advancements, there may be the risk that the needs of some stakeholders would be overheard [13,15,27,31,71,95]. This is where digital humanists can assume a crucial role in these dynamics: with a focus not primarily on profit or technological concerns, they should research and discuss how such changes impact humans and societies, preserving a critical stance towards these developments and at the same time making constructive suggestions to help engineers who often have less time to dwell on ethical concerns, or have to meet deadlines and revenue goals.…”

Section: Implications For the (Digital) Humanitiesmentioning

confidence: 99%

The rapid competitive economy of machine learning development: a discussion on the social risks and benefits

Walter

2023

AI Ethics

View full text Add to dashboard Cite

Research in artificial intelligence (AI) has started in the twentieth century but it was not until 2012 that modern models of artificial neural networks aided the machine learning process considerably so that in the past ten years, both computer vision as well as natural language processing have become increasingly better. AI developments have accelerated rapidly, leaving open questions about the potential benefits and risks of these dynamics and how the latter might be managed. This paper discusses three major risks, all lying in the domain of AI safety engineering: the problem of AI alignment, the problem of AI abuse, and the problem of information control. The discussion goes through a short history of AI development, briefly touching on the benefits and risks, and eventually making the case that the risks might potentially be mitigated through strong collaborations and awareness concerning trustworthy AI. Implications for the (digital) humanities are discussed.

show abstract

Responsible AI Systems: Who are the Stakeholders?

Abstract: For guidance on citations see FAQs.

Cited by 29 publications

References 44 publications

Managing the race to the moon: Global policy and governance in Artificial Intelligence regulation—A contemporary overview and an analysis of socioeconomic consequences

Managing the race to the moon: Global policy and governance in Artificial Intelligence regulation—A contemporary overview and an analysis of socioeconomic consequences

What ChatGPT Tells Us about Gender: A Cautionary Tale about Performativity and Gender Biases in AI

The rapid competitive economy of machine learning development: a discussion on the social risks and benefits

Contact Info

Product

Resources

About