Scoring vulnerable people: When tech turns against communities

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  Quantumrun Foresight
• February 14, 2024

Insight summary

Artificial intelligence (AI)'s expanding role in sectors like employment and healthcare could expose vulnerable communities to bias and unethical scoring practices. The increasing reliance on AI in critical areas underscores the need for diverse data and stringent regulations to prevent discrimination. This trend highlights a growing demand for transparency, fairness in AI applications, and a shift in public and governmental approaches to technology governance.

Scoring vulnerable people context

In recent years, AI has been increasingly used in various sectors, particularly employment, healthcare, and police enforcement. By 2020, over half of hiring managers in the US were incorporating algorithmic software and AI tools in recruitment, a trend that has continued to grow. The algorithms powering these platforms and systems leverage various data types, including explicit information from profiles, implicit data inferred from user actions, and behavioral analytics. However, this complex interplay of data and algorithmic decision-making introduces the risk of bias. For example, women often underrepresent their skills on resumes, and specific gendered language can influence how an algorithm evaluates a candidate's suitability. 

In healthcare, if the data used to train these algorithms is not diverse, it can lead to misdiagnosis or inappropriate treatment recommendations, particularly for underrepresented groups. Another concern is privacy and data security, as healthcare data is extremely sensitive. In policing, AI is being utilized in various forms, such as predictive policing algorithms, facial recognition technology, and surveillance systems. Several studies have highlighted that people of color are often wrongly identified by these facial recognition systems.

The regulatory landscape is evolving to address these challenges. Legislative efforts, like the Algorithmic Accountability Act of 2022, aim to mitigate algorithmic bias by requiring companies to conduct impact assessments of AI systems in critical decision-making areas. However, addressing the issue of bias in AI-driven hiring processes requires concerted efforts from multiple stakeholders. Technology developers must ensure transparency and fairness in their algorithms, companies need to acknowledge and address the limitations of these tools, and policymakers need to enforce regulations that protect against discriminatory practices. 

Disruptive impact

The long-term impact of scoring vulnerable people, mainly through systems like credit scoring and algorithmic hiring, can significantly influence social mobility and economic disparity. Credit scores, essential for determining financial credibility, often disadvantage people from lower socio-economic backgrounds. Over time, this perpetuates a cycle where disadvantaged people face further challenges in accessing essential financial services.

The impact of biased scoring systems can lead to broader social exclusion, affecting housing, employment, and access to essential services. People with lower scores or those unfairly evaluated by biased algorithms may find it difficult to secure housing or jobs, reinforcing existing social inequalities. This scenario underscores the need for more equitable scoring systems that consider the broader context of an individual's life rather than relying solely on narrow data points.

Companies, especially those in the financial and recruitment sectors, may inadvertently contribute to social stratification by relying on these biased systems. Meanwhile, governments face the challenge of ensuring regulations keep pace with technological advancements to protect vulnerable populations. They need to promote transparency and accountability in scoring systems or risk citizens losing trust in government institutions and programs.

Implications of scoring vulnerable people

Wider implications of scoring vulnerable people may include: 

• Enhanced credit scoring models incorporating alternative data, leading to improved access to financial products for historically underserved communities.
• Governments implementing stricter regulations on AI-based hiring tools, ensuring fairer employment practices across industries.
• Increased public awareness and advocacy against biased AI, resulting in more transparent and accountable technological deployments.
• Companies revising their hiring strategies, potentially reducing unconscious bias and promoting diversity in the workplace.
• Development of new industries and job roles focused on ethical AI and algorithm auditing, contributing to job market diversification.
• Increased investment in AI research to address bias and fairness, driving technological advancements that benefit a broader spectrum of society.

Questions to consider

• How might integrating more diverse datasets in AI algorithms reshape our understanding of societal fairness and equality?
• How can individuals actively contribute to or influence the development of ethical AI practices in their everyday lives and workplaces?