W3: Olatoye et al. (2024) Flashcards
(21 cards)
Paper’s aim
Provides a thorough examination of the intersection between AI and ethics within the context of corporate responsibility. It emphasises the importance of transparency in AI algorithms and decision-making processes, highlighting the need for accountable and understandable practices. Also, it addresses ethical considerations related to bias, fairness, socio-economic impact, and data privacy, urging companies to prioritise responsible AI practices to align with societal values and ethical standards
Transparency
Importance in AI decision-making processes is emphasised for fostering trust, ensuring accountability, and promoting understanding among stakeholders. Transparent AI algorithms facilitate the identification and mitigation of biases within decision-making processes. It fosters accountability by making the decision-making process traceable and understandable to stakeholders, enhancing trust in sectors with significant consequences like healthcare and finance. It empowers users by showing how decisions are reached, promoting trust and engagement with AI, particularly in e.g. customer service
Bias mitigation
Addressing biases inherent in AI algorithms is crucial to prevent discriminatory outcomes, particularly in areas like hiring, lending, and law enforcement. Using diverse and inclusive datasets is fundamental to mitigating biases
Socio-economic impacts
AI has potential to reshape employment dynamics and exacerbate inequality. This necessitates inclusive practices and ethical considerations to mitigate negative consequences. It can exacerbate existing inequalities or promote inclusivity
Accountability in AI
Can be operationalised through protocols for identifying and rectifying errors or unintended consequences promptly, showcasing a commitment to responsible and ethical AI practices
Ethical frameworks
Establishing and adhering to ethical frameworks for AI deployment is essential for building trust, particularly in sectors where ethical considerations are paramount
Biases
Can arise from historical data, human prejudices, or systemic inequalities, leading to discriminatory outcomes and reinforcing social injustices. In AI algorithms, they can manifest as disparate impact, selection bias, or confirmation bias
Biased algorithms
Can perpetuate existing disparities and erode trust in AI systems
Inclusive datasets
Accurately represent the population and help avoid skewed models that reinforce stereotypes
Ethical data collection
Involves seeking diverse perspectives, avoiding historical biases, obtaining informed consent, and engaging with affected communities
Fairness-aware approaches
Prioritise equitable outcomes and contribute to building AI systems aligned with societal values
AI’s automation capabilities
May lead to job displacement, changing skill requirements, and the digital divide. Ethical considerations include investing in reskilling and upskilling programs, prioritising inclusivity in hiring and talent development, and upholding the well-being and rights of workers
Proactive measures
Required to ensure equitable distribution of benefits and prevent widening socio-economic gaps. This involves incorporating diversity and inclusivity in AI development, prioritising accessibility and digital literacy initiatives, and engaging with affected communities to understand and address potential consequences
Corporate responsibility in AI implementation
Involves recognising the broader societal impact of AI systems, conducting thorough societal impact assessments, and prioritising transparency in decision-making processes to foster collaboration and consider diverse perspectives. It involves integrating ethical principles into organisational culture and governance, acknowledging businesses’ role as stewards of technology impacting individuals and communities
Ethical AI guidelines
Cover aspects such as fairness, transparency, accountability, and human rights. These frameworks guide decision-making, set standards, and ensure a principled approach to AI deployment
Corporate responsibility
Requires businesses to strike a balance between achieving business goals and prioritising societal well-being. This involves considering the ethical implications of AI applications, weighing potential risks, and prioritising responsible practices that contribute positively to society
Responsible AI deployment
Involves a focus on long-term sustainability rather than short-term gains, anticipating potential challenges, and proactively implementing measures to mitigate negative consequences. Collaboration with other organisations and stakeholders is vital to collectively address societal challenges posed by AI
Ethical principles
Business must adhere to them to safeguard the privact and security of sensitive information in AI applications. This includes obtaining informed consent, minimising data collection, and implementing robust anonymisation and de-identification techniques. Individuals have the right to control their personal information and be free from unwarranted surveillance or data exploitation
Ethical data-centric AI practices
Involve conducting thorough risk assessments and impact analyses to evaluate the potential consequences of data use on individuals and communities. This proactive approach ensures that businesses are aware of potential risks and take measures to mitigate them. Prioritising responsible data practices contributes to building a trustworthy AI ecosystem that respects individuals’ privacy, promotes transparency, and aligns with ethical principles
Explainable AI (XAI)
Enhances transparency into decision-making
Algorithmic audits
Assess performance across demographic groups
