MS
Marc Steen
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Algorithms and AI tools are becoming increasingly influential artefacts in commercial and governance contexts. Algorithms and AI tools are not value neutral; to some extent they must be rendered knowable and known as objects, and in their implementation and deployment, to see clearly and understand their implications for moral values, and what actions can be undertaken to optimise them in their design and use towards ethical goals, or whether they are even suitable for particular goals. Transparency is a term with variable uses and interpretations, a problem which can challenge its use in design and policy. Here, we attempt to further clarify transparency. We argue that transparency is the state of affairs that obtains when relevant and understandable information about some X is available and accessible to some target audience (A), so that this information is sufficient for A for the purpose (P). Moreover, we connect this conceptualisation with transparency’s moral value, where P is to provide an account about X’s supportive or conflicting relationship with relevant values and goals. Such teleological ends in our context here can be the ability to account for the degree to which an algorithm, process or organisation respects certain values and is conducive to (social) goals.
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Algorithms and AI tools are becoming increasingly influential artefacts in commercial and governance contexts. Algorithms and AI tools are not value neutral; to some extent they must be rendered knowable and known as objects, and in their implementation and deployment, to see clearly and understand their implications for moral values, and what actions can be undertaken to optimise them in their design and use towards ethical goals, or whether they are even suitable for particular goals. Transparency is a term with variable uses and interpretations, a problem which can challenge its use in design and policy. Here, we attempt to further clarify transparency. We argue that transparency is the state of affairs that obtains when relevant and understandable information about some X is available and accessible to some target audience (A), so that this information is sufficient for A for the purpose (P). Moreover, we connect this conceptualisation with transparency’s moral value, where P is to provide an account about X’s supportive or conflicting relationship with relevant values and goals. Such teleological ends in our context here can be the ability to account for the degree to which an algorithm, process or organisation respects certain values and is conducive to (social) goals.
Governments and companies are increasingly promoting and organizing Responsible Innovation. It is, however, unclear how the seemingly incompatible demands for responsibility, which is associated with care and caution, can be harmonized with demands for innovation, which is associated with risk-taking and speed. We turn to the tradition of virtue ethics and argue that it can be a strong accomplice to Responsible Innovation by focussing on the agential side of innovation. Virtue ethics offers an adequate response to the epistemic and moral complexity in innovation and encourages moral behaviour. We enumerate a number of virtues that people involved in Responsible Innovation would need to cultivate both related to responsibility, such as justice, anticipation, civility and inclusion, and virtues related to innovation, such as courage, dedication, curiosity and creativity. We put forward practical wisdom (phronesis) as a key virtue to regulate relevant virtues and to deal with the tension between responsibility and innovation. Practical wisdom helps an agent to find an appropriate mean in exercising and expressing the other virtues—where the mean is relative to the specific context of action and the role and abilities of the agent.
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Governments and companies are increasingly promoting and organizing Responsible Innovation. It is, however, unclear how the seemingly incompatible demands for responsibility, which is associated with care and caution, can be harmonized with demands for innovation, which is associated with risk-taking and speed. We turn to the tradition of virtue ethics and argue that it can be a strong accomplice to Responsible Innovation by focussing on the agential side of innovation. Virtue ethics offers an adequate response to the epistemic and moral complexity in innovation and encourages moral behaviour. We enumerate a number of virtues that people involved in Responsible Innovation would need to cultivate both related to responsibility, such as justice, anticipation, civility and inclusion, and virtues related to innovation, such as courage, dedication, curiosity and creativity. We put forward practical wisdom (phronesis) as a key virtue to regulate relevant virtues and to deal with the tension between responsibility and innovation. Practical wisdom helps an agent to find an appropriate mean in exercising and expressing the other virtues—where the mean is relative to the specific context of action and the role and abilities of the agent.
Responsible innovation, anticipation and responsiveness
Case studies of algorithms in decision support in justice and security, and an exploration of potential, unintended, undesirable, higher-order effects
The collection and use of personal data on citizens in the design and deployment of algorithms in the domain of justice and security is a sensitive topic. Values like fairness, autonomy, privacy, accuracy, transparency and property are at stake. Negative examples of algorithms that propagate or exacerbate biases, inequalities or injustices have received ample attention, both in academia and in popular media. To supplement this view, we will discuss two positive examples of Responsible Innovation (RI): the design and deployment of algorithms in decision support, with good intentions and careful approaches. We then explore potential, unintended, undesirable, higher-order effects of algorithms—effects that may occur despite good intentions and careful approaches. We do that by engaging with anticipation and responsiveness, two key dimensions of Responsible Innovation. We close the paper with proposing a framework and a series of tentative recommendations to promote anticipation and responsiveness in the design and deployment of algorithms in decision support in the domain of justice and security.
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The collection and use of personal data on citizens in the design and deployment of algorithms in the domain of justice and security is a sensitive topic. Values like fairness, autonomy, privacy, accuracy, transparency and property are at stake. Negative examples of algorithms that propagate or exacerbate biases, inequalities or injustices have received ample attention, both in academia and in popular media. To supplement this view, we will discuss two positive examples of Responsible Innovation (RI): the design and deployment of algorithms in decision support, with good intentions and careful approaches. We then explore potential, unintended, undesirable, higher-order effects of algorithms—effects that may occur despite good intentions and careful approaches. We do that by engaging with anticipation and responsiveness, two key dimensions of Responsible Innovation. We close the paper with proposing a framework and a series of tentative recommendations to promote anticipation and responsiveness in the design and deployment of algorithms in decision support in the domain of justice and security.
This article presents a conceptual investigation into the value impacts and relations of algorithms in the domain of justice and security. As a conceptual investigation, it represents one step in a value sensitive design based methodology (not incorporated here are empirical and technical investigations). Here, we explicate and analyse the expression of values of accuracy, privacy, fairness and equality, property and ownership, and accountability and transparency in this context. We find that values are sensitive to disvalue if algorithms are designed, implemented or deployed inappropriately or without sufficient consideration for their value impacts, potentially resulting in problems including discrimination and constrained autonomy. Furthermore, we outline a framework of conceptual relations of values indicated by our analysis, and potential value tensions in their implementation and deployment with a view towards supporting future research, and supporting the value sensitive design of algorithms in justice and security.
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This article presents a conceptual investigation into the value impacts and relations of algorithms in the domain of justice and security. As a conceptual investigation, it represents one step in a value sensitive design based methodology (not incorporated here are empirical and technical investigations). Here, we explicate and analyse the expression of values of accuracy, privacy, fairness and equality, property and ownership, and accountability and transparency in this context. We find that values are sensitive to disvalue if algorithms are designed, implemented or deployed inappropriately or without sufficient consideration for their value impacts, potentially resulting in problems including discrimination and constrained autonomy. Furthermore, we outline a framework of conceptual relations of values indicated by our analysis, and potential value tensions in their implementation and deployment with a view towards supporting future research, and supporting the value sensitive design of algorithms in justice and security.
Co-design
Practices, challenges and lessons learned
Conference paper
(2008)
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Marc Steen, Jenny De Boer, Lilliane Kuiper-Hoyng, Froukje Sleeswijk Visser
This paper is a proposal to organize a workshop about co-design during the during MobileHCI 2008 Conference. Main goals of the workshop are to discuss co-design practices and to articulate challenges and lessons learned.
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This paper is a proposal to organize a workshop about co-design during the during MobileHCI 2008 Conference. Main goals of the workshop are to discuss co-design practices and to articulate challenges and lessons learned.