AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing

Berendt, Bettina

doi:10.1515/pjbr-2019-0004

Cited by 76 publications

(63 citation statements)

References 34 publications

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“…AI is also discussed in relation to "social good" [21,[29][30][31][32][33][34][35][36] and other words with similar connotation such as "common good" [37] and "for good" [38]. There are subfields which include "data for social good" [94,95], "IT for social good" [96], and "computer for social good" [97][98][99].…”

Section: The Issue Of Social Goodmentioning

confidence: 99%

“…There are subfields which include "data for social good" [94,95], "IT for social good" [96], and "computer for social good" [97][98][99]. The ethical framework for "AI for the Social Good" defines AI as "addressing societal challenges, which have not yet received significant attention by the AI community or by the constellation of AI sub-communities, [the use of] AI methods to tackle unsolved societal challenges in a measurable manner" [37]. In a workshop focusing explicitly on "AI for social good", the term "social good" is described as follows: "[social good] is intended to focus AI research on areas of endeavor that are to benefit a broad population in a way that may not have direct economic impact or return, but which will enhance the quality of life of a population of individuals through education, safety, health, living environment, and so forth" [36].…”

Section: The Issue Of Social Goodmentioning

confidence: 99%

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Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Lillywhite

Wolbring

2020

Societies

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Artificial intelligence (AI) and machine learning (ML) advancements increasingly impact society and AI/ML ethics and governance discourses have emerged. Various countries have established AI/ML strategies. “AI for good” and “AI for social good” are just two discourses that focus on using AI/ML in a positive way. Disabled people are impacted by AI/ML in many ways such as potential therapeutic and non-therapeutic users of AI/ML advanced products and processes and by the changing societal parameters enabled by AI/ML advancements. They are impacted by AI/ML ethics and governance discussions and discussions around the use of AI/ML for good and social good. Using identity, role, and stakeholder theories as our lenses, the aim of our scoping review is to identify and analyze to what extent, and how, AI/ML focused academic literature, Canadian newspapers, and Twitter tweets engage with disabled people. Performing manifest coding of the presence of the terms “AI”, or “artificial intelligence” or “machine learning” in conjunction with the term “patient”, or “disabled people” or “people with disabilities” we found that the term “patient” was used 20 times more than the terms “disabled people” and “people with disabilities” together to identify disabled people within the AI/ML literature covered. As to the downloaded 1540 academic abstracts, 234 full-text Canadian English language newspaper articles and 2879 tweets containing at least one of 58 terms used to depict disabled people (excluding the term patient) and the three AI terms, we found that health was one major focus, that the social good/for good discourse was not mentioned in relation to disabled people, that the tone of AI/ML coverage was mostly techno-optimistic and that disabled people were mostly engaged with in their role of being therapeutic or non-therapeutic users of AI/ML influenced products. Problems with AI/ML were mentioned in relation to the user having a bodily problem, the usability of AI/ML influenced technologies, and problems disabled people face accessing such technologies. Problems caused for disabled people by AI/ML advancements, such as changing occupational landscapes, were not mentioned. Disabled people were not covered as knowledge producers or influencers of AI/ML discourses including AI/ML governance and ethics discourses. Our findings suggest that AI/ML coverage must change, if disabled people are to become meaningful contributors to, and beneficiaries of, discussions around AI/ML.

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Section: The Issue Of Social Goodmentioning

confidence: 99%

Section: The Issue Of Social Goodmentioning

confidence: 99%

Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Lillywhite

Wolbring

2020

Societies

View full text Add to dashboard Cite

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“…While there are many definitions depending upon the specific context, the researcher has chosen the following as most relevant to the current study: ‘AI refers to a field of computer science dedicated to the creation of systems performing tasks that usually require human intelligence’ (Jakhar, and Kaur, 2020, p. 1). According to Berendt (2019), the immediate output of AI is knowledge, which may be useful and understood by either a person or machine. In addition, AI has a social impact, including large social networks, search engines, and national and international surveillance programs (Berendt, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…According to Berendt (2019), the immediate output of AI is knowledge, which may be useful and understood by either a person or machine. In addition, AI has a social impact, including large social networks, search engines, and national and international surveillance programs (Berendt, 2019). It is considered as a social good for the transportation of knowledge and information.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence tools and perspectives of university librarians: An overview

Ali

Naeem

Bhatti

2020

Business Information Review

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The main purpose of this paper is to assess and examine the possible application of Artificial Intelligence (AI) tools in Pakistani academic libraries, particularly those areas of library technical and library user services where AI could be applied in the near future. A secondary purpose is to bring the library perspective on AI to the forefront of the scholarly world. This is a self-exploratory study, in which a qualitative approach interview has been conducted with 10 chief librarians/library heads (5 public + 5 private sectors) from universities regarding their views on the adoption of artificial intelligence tools in Pakistani academic libraries. Results are tabulated in a descriptive format. Librarians are aware of AI technologies. Services based on Natural Language Processing (NLP) are used in libraries, e.g. Google Assistant, Voice Searching, and Google Translate. Pattern recognition methods, such as text data mining, are also used to retrieve library material and conduct online searching. Big data is accessed via services such as cloud computing, OneDrive, and Google Drive. There is a very low level of awareness of robotics and chatbots. This study provides librarians with suggestions as to how AI tools could be used in libraries which either have yet to adopt AI technologies or wish to implement more advanced tools. Pakistani library schools could collaborate with computer science departments to establish AI Labs in the respective library and information science (LIS) departments/libraries. AI challenges funding and technological skills are the key problem to implement with AI in the University Libraries.

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“…This distinctively German vision is primarily communicated to the public by the German government and the German media, which are the two leading actors that shape public opinion about the German future of AI. Both government and media create and enforce frames to explain what AI means and how society is destined to look in AI futures (Carbonell et al 2016;Caruso 2018;Berendt 2019;Glazier and Boydstun 2012;Kim et al 2017).…”

Section: Introductionmentioning

confidence: 99%

The making of AI society: AI futures frames in German political and media discourses

Köstler

Ossewaarde

2021

AI & Soc

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In this article, we shed light on the emergence, diffusion, and use of socio-technological future visions. The artificial intelligence (AI) future vision of the German federal government is examined and juxtaposed with the respective news media coverage of the German media. By means of a content analysis of frames, it is demonstrated how the German government strategically uses its AI future vision to uphold the status quo. The German media largely adapt the government´s frames and do not integrate alternative future narratives into the public debate. These findings are substantiated in the framing of AI futures in policy documents of the German government and articles of four different German newspapers. It is shown how the German past is mirrored in the German AI future envisioned by the government, safeguarding the present power constellation that is marked by a close unity of politics and industry. The German media partly expose the government´s frames and call for future visions that include fundamentally different political designs less influenced by the power structures of the past and present.

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AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing

Cited by 76 publications

References 34 publications

Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Artificial intelligence tools and perspectives of university librarians: An overview

The making of AI society: AI futures frames in German political and media discourses

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