Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality

Ryan, Mark; Antoniou, Josephina; Brooks, Laurence; Jiya, Tilimbe; Macnish, Kevin; Stahl, Bernd Carsten

doi:10.1007/s11948-021-00293-x

Cited by 31 publications

(14 citation statements)

References 50 publications

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“…AstraZeneca's ethics principles aim to help employees and partners navigate the risks associated with AI systems. Yet principles alone cannot ensure that such systems are designed and used ethically (Mittelstadt, 2019), and their implementation is never straightforward (Ryan et al, 2021). Moreover, like many other multinational corporations, AstraZeneca is a decentralized organization.…”

Section: Case Study-astrazeneca's Ai Governance Journeymentioning

confidence: 99%

Challenges and best practices in corporate AI governance: Lessons from the biopharmaceutical industry

Mökander

Sheth

Gersbro-Sundler

et al. 2022

Front. Comput. Sci.

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While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management.

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Section: Case Study-astrazeneca's Ai Governance Journeymentioning

confidence: 99%

Challenges and best practices in corporate AI governance: Lessons from the biopharmaceutical industry

Mökander

Sheth

Gersbro-Sundler

et al. 2022

Front. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Furthermore, the data collected from farms can raise concerns about data ownership i.e., who owns the farm data and who has control over the use of farm data (Mark, 2019). This can lead to a power imbalance between farmers and technology or service providers (Ryan et al, 2021a). Accountability is another issue when it comes to AI-technologies; for instance, if AI systems spray substantial amounts of water or pesticides due to errors in the system, who will be responsible for the loss of harvest?…”

Section: Introductionmentioning

confidence: 99%

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Dara

Fard

Kaur

2022

Front. Artif. Intell.

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Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.

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“…Recent studies have identified and discussed ethical issues that arise from AI and Big Data either on a normative or empirical level (Jobin et al, 2019;Müller, 2020;Ryan et al, 2021;Stahl et al, 2021). What is missing from existing literature however, is a comprehensive understanding of and engagement with, the challenges that arise when such attempts are made to mitigate the risks of Big Data and AI while ensuring that they are harnessed for the benefit and well-being of society.…”

Section: Introductionmentioning

confidence: 99%

Democracy Under Attack: Challenges of Addressing Ethical Issues of AI and Big Data for More Democratic Digital Media and Societies

Christodoulou

Iordanou

2021

Front. Polit. Sci.

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The potency and potential of digital media to contribute to democracy has recently come under intense scrutiny. In the context of rising populism, extremism, digital surveillance and manipulation of data, there has been a shift towards more critical approaches to digital media including its producers and consumers. This shift, concomitant with calls for a path toward digital well-being, warrants a closer investigation into the study of the ethical issues arising from Artificial Intelligence (AI) and Big Data. The use of Big Data and AI in digital media are often incongruent with fundamental democratic principles and human rights. The dominant paradigm is one of covert exploitation, erosion of individual agency and autonomy, and a sheer lack of transparency and accountability, reminiscent of authoritarian dynamics rather than of a digital well-being with equal and active participation of informed citizens. Our paper contributes to the promising research landscape that seeks to address these ethical issues by providing an in-depth analysis of the challenges that stakeholders are faced with when attempts are made to mitigate the negative implications of Big Data and AI. Rich empirical evidence collected from six focus groups, across Europe, with key stakeholders in the area of shaping ethical dimensions of technology, provide useful insights into elucidating the multifaceted dilemmas, tensions and obstacles that stakeholders are confronted with when being tasked to address ethical issues of digital media, with a focus on AI and Big Data. Identifying, discussing and explicating these challenges is a crucial and necessary step if researchers and policymakers are to envisage and design ways and policies to overcome them. Our findings enrich the academic discourse and are useful for practitioners engaging in the pursuit of responsible innovation that protects the well-being of its users while defending the democratic foundations which are at stake.

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Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality

Cited by 31 publications

References 50 publications

Challenges and best practices in corporate AI governance: Lessons from the biopharmaceutical industry

Challenges and best practices in corporate AI governance: Lessons from the biopharmaceutical industry

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Democracy Under Attack: Challenges of Addressing Ethical Issues of AI and Big Data for More Democratic Digital Media and Societies

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