Carissa Véliz: Predictive technologies require enlightened decision-making, algorithmic hiring can perpetuate biases, and academic fraud undermines integrity | Big Technology

Predictive algorithms in hiring and finance risk perpetuating systemic biases and unfair decision-making.

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Add us on Google by Editorial Team Apr. 22, 2026

Key takeaways

• Predictive technologies require a more enlightened approach to decision-making.
• The unpredictability of significant life events limits the effectiveness of predictive models.
• Self-fulfilling prophecies in AI can lead to unnoticed unfairness in job applications.
• Algorithmic job filtration may overlook qualified candidates due to resume quirks.
• Algorithmic hiring systems can create unfair advantages and incentivize negative behaviors.
• Academic fraud is a significant issue among successful individuals in academia.
• Heavy reliance on personality assessments in hiring can exclude great candidates.
• Predictive models in loan applications can lead to unjust rejections without accountability.
• Machine learning algorithms in mortgage applications raise concerns about fairness.
• The mortgage system’s reliance on accurate risk assessment is complicated by biased algorithms.
• Algorithms in hiring processes can perpetuate systemic biases and unfairness.
• The ethical implications of predictive technologies are critical in financial services.
• Understanding the limitations of predictive models is essential for fair decision-making.
• The intersection of philosophy and technology highlights the societal impacts of prediction.

Guest intro

Carissa Véliz is an Associate Professor in Philosophy at the Institute for Ethics in AI at the University of Oxford, where she researches privacy, AI ethics, and public policy. She is the author of Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI, as well as Privacy Is Power, which was named an Economist Book of the Year, and The Ethics of Privacy and Surveillance. Véliz advises policymakers and companies worldwide on AI and ethics, including the UK Parliament, US Congress, and the European Commission, and serves as a board member of the Proton Foundation alongside Sir Tim Berners-Lee.

The need for enlightened use of predictive technologies

• We should be more enlightened about the use of predictions in decision-making.

  — Carissa Véliz

• Predictive algorithms can impact fairness in sensitive areas like employment and justice.

• I think we’re being so incredibly naive and in some cases sure we shouldn’t use prediction.

  — Carissa Véliz

• Understanding the implications of predictive algorithms is crucial for fair outcomes.
• Predictive technologies can influence decision-making in various systems.

• We should be much more enlightened about it.

  — Carissa Véliz

• The ethical use of predictive technologies is essential for societal fairness.
• Awareness of predictive limitations can prevent misuse in critical areas.

The unpredictability of life events and predictive limitations

• The most important events in your life… are the ones that are the most unpredictable.

  — Carissa Véliz

• Unpredictability affects decision-making and limits predictive model effectiveness.

• It’s the curves that are really hard to see and in some cases impossible.

  — Carissa Véliz

• Predictive algorithms struggle with the inherent unpredictability of significant events.
• Understanding unpredictability is key to improving predictive technologies.
• Life’s unpredictable events challenge the reliability of predictive models.
• Emphasizing unpredictability can lead to more realistic expectations of predictions.
• Predictive limitations highlight the need for cautious application in decision-making.

Self-fulfilling prophecies in AI and job application biases

• Self-fulfilling prophecies are like the perfect crime.

  — Carissa Véliz

• AI in job applications can create unnoticed unfairness and systemic biases.

• It’s like a murder weapon that disappears upon striking.

  — Carissa Véliz

• The hidden biases of AI systems have significant consequences in the job market.

• It creates no error signals; we will never know how that person would have fared.

  — Carissa Véliz

• Understanding AI’s impact on job applications is crucial for fairness.
• AI can lead to self-fulfilling prophecies that perpetuate systemic biases.
• Awareness of AI’s potential biases can improve job application processes.

Algorithmic job filtration and the exclusion of qualified candidates

• Algorithmic job filtration can overlook qualified candidates due to quirks in their resumes.

  — Carissa Véliz

• Algorithms assess resumes and can introduce potential biases.

• I’ve met someone who is really good at their job… they get filtered out.

  — Carissa Véliz

• Understanding algorithmic limitations is crucial for diverse candidate inclusion.

• There might be something in his CV that makes him look quirky, and algorithms don’t like quirky.

  — Carissa Véliz

• Algorithmic hiring processes can impact diverse candidates negatively.
• Awareness of algorithmic biases can improve hiring practices.
• Algorithms can overlook qualified candidates due to resume quirks.

The unfair advantages and negative behaviors in algorithmic hiring

• Algorithmic hiring systems can create unfair advantages.

  — Carissa Véliz

• These systems may incentivize negative behaviors among job seekers.

• Maybe you have a better advantage if you try to break out of that system.

  — Carissa Véliz

• Understanding the implications of algorithmic hiring is crucial for fairness.

• People have agency at the end of the day.

  — Carissa Véliz

• Algorithmic hiring practices can affect job seekers’ behaviors.
• Awareness of algorithmic hiring’s impact can lead to fairer practices.
• Algorithmic systems can create unfair advantages and incentivize negative behaviors.

Academic fraud and ethical issues in competitive environments

• There is a serious problem of academic fraud among successful individuals in academia.

  — Carissa Véliz

• Competitive environments can encourage unethical behaviors.

• We have a serious problem of fraud of people who are very well known.

  — Carissa Véliz

• Understanding the challenges in academia is crucial for ethical practices.

• People who have been very successful and who have fetched their data.

  — Carissa Véliz

• Awareness of academic fraud can improve research integrity.
• Academic fraud is a significant issue linked to competitive environments.
• Ethical issues in academia highlight the need for integrity in research.

Personality assessments in hiring and their impact on talent acquisition

• Relying heavily on personality assessments can filter out great candidates.

  — Carissa Véliz

• Personality assessments can exclude qualified candidates due to flawed methods.

• One little misstep on a multiple choice and poorly worded question filters them out.

  — Carissa Véliz

• Understanding the implications of personality assessments is crucial for hiring.

• I think that’s actually a bad thing for employers as well.

  — Carissa Véliz

• Awareness of assessment limitations can improve talent acquisition.
• Personality assessments can impact talent acquisition negatively.
• Flawed assessment methods can exclude great candidates from the hiring pool.

Predictive models in loan applications and accountability issues

• Loan applications based on predictions can lead to unjust rejections without accountability.

  — Carissa Véliz

• Predictive models can shroud injustice and lessen accountability in financial services.

• If you apply and I reject your application on the basis of a prediction.

  — Carissa Véliz

• Understanding predictive model limitations is crucial for fair financial services.

• You cannot prove it to be false, and so it’s a way to shroud a lot of injustice.

  — Carissa Véliz

• Awareness of accountability issues can improve loan application processes.
• Predictive models can lead to unjust rejections without accountability.
• Accountability issues in predictive models highlight the need for fair practices.

Machine learning in mortgage applications and fairness concerns

• Machine learning algorithms in mortgage applications can categorize applicants based on their likelihood to repay loans.

  — Carissa Véliz

• These algorithms raise concerns about fairness in financial decision-making.

• They’ll put you in a category in terms of likeliness to pay back a loan.

  — Carissa Véliz

• Understanding the implications of machine learning is crucial for fairness.

• If you have an algorithm that is not very accurate and that is not very fair.

  — Carissa Véliz

• Awareness of fairness concerns can improve mortgage application processes.
• Machine learning algorithms can impact fairness in lending.
• Fairness concerns in machine learning highlight the need for ethical practices.

The mortgage system’s reliance on accurate risk assessment

• The mortgage system relies on banks’ ability to assess risk accurately.

  — Carissa Véliz

• Biased algorithms complicate risk assessment in lending.

• The mortgage system can’t exist because we give people all this money.

  — Carissa Véliz

• Understanding the role of technology in risk assessment is crucial for lending.

• If a bank can use this software to determine more effectively through prediction.

  — Carissa Véliz

• Awareness of biased algorithms can improve risk assessment practices.
• Accurate risk assessment is critical for the mortgage system’s success.
• Biased algorithms complicate the mortgage system’s reliance on risk assessment.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.