Human‐centered Design of Artificial Intelligence

Margetis, George; Ntoa, Stavroula; Antona, Margherita; Stephanidis, Constantine

doi:10.1002/9781119636113.ch42

Cited by 45 publications

(14 citation statements)

References 101 publications

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“…In their chapter on HCD of AI in the Handbook of Human Factors and Ergonomics, Margetis et al state that "A core concept of HCD is that of actively involving end-users and appropriate stakeholders in the process. In the context of AI, this means placing humans in the loop, not only through meaningful human control [256], but also through their active participation in the preparation, learning, and decision-making phases of AI [257]." Human-centered AI (HCAI) is an emerging area of scholarship that reconceptualizes HCD in the context of AI, providing human-centered AI design metaphors and suggested governance structures to develop reliable, safe, and trustworthy AI systems [258].…”

Section: /77mentioning

confidence: 99%

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz¹,

Vassilev²,

Greene³

et al. 2022

179

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Bias is neither new nor unique to AI and it is not possible to achieve zero risk of bias in an AI system. NIST intends to develop methods for increasing assurance, GOVERNANCE and practice improvements for identifying, understanding, measuring, managing, and reducing bias. To reach this goal, techniques are needed that are flexible, can be applied across contexts regardless of industry, and are easily communicated to different stakeholder groups. To contribute to the growth of this burgeoning topic area, NIST will continue its work in measuring and evaluating computational biases, and seeks to create a hub for evaluating socio-technical factors. This will include development of formal guidance and standards, supporting standards development activities such as workshops and public comment periods for draft documents, and ongoing discussion of these topics with the stakeholder community.

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Section: /77mentioning

confidence: 99%

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz¹,

Vassilev²,

Greene³

et al. 2022

179

View full text Add to dashboard Cite

show abstract

“…Naturally, there is no guarantee that principles will automatically be shared in real-world contexts [242], so it is an open question of how to embed these into a framework. Actively learning human values via experience is one possibility; in such a case, human involvement in the data collection phase is considered essential, especially for ethically complex problems [190]. Additionally, an AutonoML system could potentially produce a set of ML solutions, rather than any single optimum, on which humans would preference-vote.…”

Section: Roles and Modes In Relation To Autonomous Open-world Systemsmentioning

confidence: 99%

“…So, assuming alignment is possible, what would HCI look like in a collaborative relationship? One work states that the critical human roles in a collective process would include defining problem context, exploring data/knowledge used by a system, understanding the outcomes of an ML application, providing a correct interpretation of the results, and intervening when necessary via feedback [190]. Such feedback can help AutonoML systems extend their knowledge base and enhance decision-making efficiency and effectiveness.…”

Section: Roles and Modes In Relation To Autonomous Open-world Systemsmentioning

confidence: 99%

“…This facet expands on the requirements that collaborative HCI requires. After all, with AutonoML systems being granted greater agency on account of their presumed superior performance at conducting open-world ML, a key issue is how best to ensure that automated behaviours/actions are for the sake of humans [190], e.g. ethically compliant.…”

Section: Roles and Modes In Relation To Autonomous Open-world Systemsmentioning

confidence: 99%

“…The two aims do not necessarily have to compete, but, as Industry 5.0 attempts to recognise, technical performance is a narrow view of good performance. It is thus likely that human-centred design (HCD) principles [190] will become increasingly important in the coming years. The main activities of HCD for AI systems include the specification and comprehension of contexts and requirements, the latter being held by business stakeholders and end-users.…”

Section: Potential Research Directionsmentioning

confidence: 99%

See 2 more Smart Citations

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

Khuat¹,

Kedziora²,

Gabrys³

2022

Preprint

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As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the 'how' and 'why' of human-computer interaction (HCI) within these frameworks, both current and expected. Such a discussion is necessary for optimal system design, leveraging advanced data-processing capabilities to support decision-making involving humans, but it is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy. Within this context, we focus on the following questions: (i) How does HCI currently look like for state-of-the-art AutoML algorithms, especially during the stages of development, deployment, and maintenance? (ii) Do the expectations of HCI within AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and broad acceptance? (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI evolve? To consider these questions, we project existing literature in HCI into the space of AutoML; this connection has, to date, largely been unexplored. In so doing, we review topics including user-interface design, human-bias mitigation, and trust in artificial intelligence (AI). Additionally, to rigorously gauge the future of HCI, we contemplate how AutoML may manifest in effectively open-ended environments. This discussion necessarily reviews projected developmental pathways for AutoML, such as the incorporation of reasoning, although the focus remains on how and why HCI may occur in such a framework rather than on any implementational details. Ultimately, this review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.

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From a Workshop to a Framework for Human-Centered Artificial Intelligence

Degen

Ntoa

2021

Lecture Notes in Computer Science

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Human‐centered Design of Artificial Intelligence

Cited by 45 publications

References 101 publications

Towards a standard for identifying and managing bias in artificial intelligence

Towards a standard for identifying and managing bias in artificial intelligence

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

From a Workshop to a Framework for Human-Centered Artificial Intelligence

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