New social good platform acquires Network for Good

The scientific enterprise reflects society at large, and as such it actively disadvantages minority groups. From an ethical perspective, this system is unacceptable as it actively undermines principles of justice and social good, as well as the research principles of openness and public responsibility. Further, minority social scientists lead to better overall scientific products, meaning a diverse scientific body can also be considered an instrumental good. Thus, centering minority voices in science is an ethical imperative. This paper outlines what can be done to actively center these scientists, including changing the way metrics are used to assess the performance of individual scientists and altering the reward structure within academic science to promote heterogenous research groups.

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Session details: Paper Session: Computing for Social Good

Proceedings of the 51st ACM Technical Symposium on Computer Science Education ◽

10.1145/3385553 ◽

2020 ◽

Author(s):

Sathiamoorthy Manoharan

Keyword(s):

Paper Session ◽

Social Good

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Harness Technology for Social Good

10.1093/oso/9780190858988.003.0009 ◽

2018 ◽

Cited By ~ 1

Author(s):

Stephanie Cosner Berzin ◽

Claudia J. Coulton

Keyword(s):

Digital Technology ◽

Human Services ◽

Social Problems ◽

New Technologies ◽

Social Spending ◽

Social Good ◽

Speed Up ◽

Effective Programs

Innovative applications of new digital technology present opportunities for social and human services to reach more people with greater impact on our most vexing social problems. These new technologies can be deployed to more strategically target social spending, speed up the development of effective programs, and bring a wider array of help to more individuals and communities.

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Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽

10.1007/s43681-021-00038-3 ◽

2021 ◽

Author(s):

Steven Umbrello ◽

Ibo van de Poel

Keyword(s):

Artificial Intelligence ◽

Contact Tracing ◽

Use Case ◽

Value Sensitive Design ◽

Specific Design ◽

Established Method ◽

Social Good ◽

Do No Harm ◽

Design Requirements ◽

Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

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