What does GenAI mean for behavioural & social scientists?

Alexandria Chesterfield published an interesting summary on Linked In covering two academic articles about the impact of AI on social science research. She highlighted a range of topics that distinguished between AI for Social Science and the Social Science of AI. This covers the use of AI to generate hypotheses, do analysis and coding, improve surveys and even mimic behavior in groups.

Forbes article – HR adapting to AI

Bryan Robinson, a contributor at Forbes, wrote Embrace the Fact that AI Will Dominate Careers in 2024, Experts Say. He suggests that AI has become an essential business tool in human resources and is poised to change every system within HR. With these technological advances, reskilling will be needed – and learning agility and soft skills will become a critical requirement.

Why Social Science? blog post

Carlotta Arthur at the Why Social Science? blog wrote about how social science can contribute to AI that benefits society. Includes connection to public policy work at Division of Behavioral and Social Sciences and Education (DBSSE) at the National Academies of Sciences, Engineering and Medicine. Includes a mention of the work done on AI in education systems with the Board on Science Education (BOSE).

PDRI by Pearson Generative AI in Assessment and Job Analysis Research

PDRI by Pearson announced the start of new research into the use of generative AI in the development and use of assessments.

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When most people think of AI, they instantly think of applications like Chat GPT that work off of generative AI. With generative AI new content is created based on patterns from underlying data.

In the workplace, generative AI might be used to create first drafts of press releases or reports, brainstorms ideas, create art for a project, draft training modules or build simulations and support other creative work.

But there is another type of AI.

Definition of Predictive AI

Predictive AI, on the other hand, is focused on making predictions or forecasts based on historical data patterns. It leverages statistical algorithms and machine learning models to identify trends, correlations, and relationships within datasets, allowing it to make informed predictions about future events in broad areas like finance, stock markets, health care, and supply chains.

Predictive AI, when applied to employees at work, involves utilizing artificial intelligence systems to analyze historical data and make informed predictions about future workforce-related outcomes.

In some workplace scenarios, the use of AI can help predict trends that support strategic decision making. For example, predictive models can analyze various factors contributing to employee turnover, such as job satisfaction, work-life balance, or career development opportunities. This helps organizations implement targeted retention strategies to keep valuable employees and forecast future workforce needs. By analyzing historical data on staffing levels, skills, and market trends, organizations can strategically plan for changes in demand, skill requirements, and industry shifts.

Risks of Predictive AI

Predictive AI gets more complicated when it is applied to individuals. In some cases, AI is being used to predict the ability of an individual to perform a specific job. It’s being incorporated into selection assessments, promotional decisions, resume screening, job description writing, candidate attraction and similar human resources practices.

Using AI this way introduces risk because it is making a consequential decision about a person and that AI may or may not have been designed well. 

Poor AI design can come from bad training or testing data or poor algorithms or poor modeling and decisions made while designing the AI. Sometimes that means that poorly designed AI is making decisions about who gets interviews, who gets jobs, who gets promotions, etc.

That’s a problem. And a risk.

Four Ethical & Legal Risk Areas

From a high-level perspective, there are four areas to consider from an ethical and legal risk perspective when considering the impact of predictive AI.

The good news is that some of these risks can be mitigated by following good scientific practices, conducting research, asking thorough questions, using inclusive samples and keeping the rights of the individual in mind in all situations. These tendencies come naturally to most consulting psychologists.  

Area 1: Fairness and Bias

If historical data used to train the model contains biases, the AI system may inadvertently perpetuate and exacerbate these biases, leading to discriminatory outcomes. The bias in the data may or may not be visible when the AI is first used.

Area 2: Transparency and Explainability

 Many AI algorithms operate as complex “black boxes,” making it challenging to understand how decisions are reached. This lack of transparency can be a significant ethical concern, especially when individuals are affected by AI-driven decisions.

 Consulting psychologists should advocate for transparency in AI models and demand explanations for how predictions are made to ensure accountability and fairness.

This can be particularly challenging when working with vendors who do not want to share proprietary information.

Area 3: Privacy Concerns

Predictive AI often relies on extensive personal data, raising concerns about individual privacy. It is essential for consulting psychologists to consider the ethical implications of collecting and using sensitive information without compromising individuals’ privacy rights.

Compliance with data protection regulations and ethical guidelines becomes crucial in safeguarding individuals’ privacy in the context of predictive AI applications.

Area 4: Legal Compliance

The use of predictive AI in employment decisions must align with existing labor laws and regulations. Failing to comply with legal standards can result in severe consequences for organizations, including legal actions and reputational damage.

Consulting psychologists should stay informed about evolving legal frameworks related to AI applications in employment and ensure that their practices adhere to these standards.

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One point that jumped out to me was that artificial intelligence systems are shaping the world but they are being designed by small minority. This can lead to bias.

Another key point, made by David Luxton, PhD, a clinical psychologist and an affiliate professor at the University of Washington’s School of Medicine is that there are two types of trust in AI: one type of trust about the system being safe and a second type of trust in how the humans are using the AI.

The article linked below also talks about video games, digital impact and social media.

#artificialintelligence#consultingpsychology#psychology

https://www.apa.org/news/apa/2024/psychology-shape-future-technology

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