📌 Algorithmic hiring tools: the need for global policy

AI case studies | How AI is transforming the world?

New email: Wild Intelligence, March 29th, “Algorithmic hiring tools: the need for global policy”

Hey there, Yael!

Can algorithmic hiring systems take over traditional process and be fair?

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Image generated with Gemini by Google

Many Fortune 500 companies use algorithmic hiring systems, which rely on machine learning to predict candidate performance with less human input.

Although they significantly determine access to work opportunities, algorithmic hiring systems can substantially impact fairness and equality.

A review of 36 algorithmic hiring tools was conducted as part of the Institute for the Future of Work's new research project to create an algorithmic impact assessment protocol.

These assessments are based on data that the candidate voluntarily and knowingly submitted to make predictions about their job performance in a given role.

▸ It is essential to understand the current landscape, how to make better decisions and what really necessitates global policy. More below.

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What is important?

Algorithmic hiring is the use of computer programs to automate parts of the job selection process. These programs can scan resumes, conduct initial interviews, and even assess a candidate's skills.

Here's a breakdown of the pros and cons of algorithmic hiring:

Pros

• Reduced bias: By relying on pre-defined criteria, algorithms can help reduce unconscious bias in the hiring process.

• Efficiency: Screening a large pool of applicants can be faster with an algorithm.

• Better matches: Algorithms can identify candidates with specific skills and experience.

Cons

• Bias creep: Algorithms can inherit bias from the data they're trained on, leading to unfair screening.

• Limited scope: Algorithms might miss out on good candidates who don't perfectly match the criteria.

• Dehumanization: Relying solely on algorithms can make the hiring process feel impersonal.

Overall, algorithmic hiring is a complex issue. While it has the potential to improve efficiency and reduce bias, it's important to be aware of the limitations and potential downsides.

The challenge

Arguably the biggest challenge of algorithmic hiring is bias.

There are two main ways this bias can creep in:

1. Biased data: Algorithms are trained on historical data, which often reflects past biases in hiring. For example, if a company has historically favored men for leadership roles, an algorithm trained on that data might unconsciously prioritize male candidates.

2. Limited evaluation: Algorithms typically focus on quantifiable factors like keywords in resumes or scores on skills tests. They might miss out on a candidate's soft skills, creativity, or experiences that don't fit neatly into the criteria.

This bias can lead to unfair hiring practices and perpetuate discrimination against certain groups. It's important for companies to be aware of this challenge and take steps to mitigate it.

An extended view

Here are some specific examples to illustrate the pros and cons of algorithmic hiring:

Pros

• Reduced bias in resume screening: Imagine a role requiring a strong data analyst. Traditionally, a recruiter might unconsciously favor resumes with "MBA" over those with a relevant coding bootcamp experience. An algorithm set to focus on required skills (e.g., SQL, Python) can give both candidates a fair shot.

• Efficiency in high-volume hiring: For large companies with many entry-level positions (e.g., customer service reps), sifting through thousands of resumes can be overwhelming. Algorithms can quickly scan for keywords and basic qualifications, allowing recruiters to focus on the most promising candidates.

• Identifying hidden gems: Sometimes, a great candidate's resume might not perfectly match the keywords on a job posting. An algorithm that analyzes skills assessments or coding exercises can unearth these hidden gems with the potential to excel in the role.

Cons

• Bias in algorithmic scoring: If an algorithm is trained on data from a company that has historically under-hired women in engineering roles, it might prioritize resumes with traditionally "masculine" language (e.g., "assertive"). This can unfairly disadvantage qualified female candidates.

• Missing out on soft skills: Algorithms struggle to assess a candidate's communication skills, teamwork abilities, or cultural fit. These "soft skills" can be crucial for success in many positions, and a purely algorithmic evaluation might miss out on a great candidate who interviews poorly but would thrive in the actual role.

• Dehumanizing the process: Relying solely on algorithms can make the hiring process feel impersonal for candidates. Job seekers might feel like they are just data points being run through a machine, leading to a negative perception of the company.

The Future of Algorithmic hiring tools

Technological advancements:

• Breakthroughs in AI: Imagine AI surpassing human intelligence and leading to significant advancements in various fields.

• Human augmentation: Perhaps brain-computer interfaces or other technologies enhance human capabilities.

• Space exploration: Will we colonize Mars or establish lunar bases? How will space travel evolve?

Societal shifts:

• Impact of automation: How will widespread automation affect the job market?

• Resource management: Can we develop sustainable solutions for energy, water, and food production?

• Global cooperation: Will we see increased international collaboration to address global challenges?

Unforeseen developments:

• Breakthroughs in biological sciences: Genetic engineering, cures for major diseases, or life extension technologies.

• Emergence of new threats: Existential risks from pandemics, natural disasters, or even advanced AI.

• Unexpected discoveries: Perhaps we find alien life, uncover a new fundamental force of nature, or make a game-changing archaeological find.

Here are some additional brainstorming techniques you can use:

• Future scenarios: Imagine two or three contrasting possibilities for the future (utopia, dystopia, etc.).

• Trend spotting: Identify current trends and extrapolate them to see how they might shape the future.

• Wishful thinking: Imagine a perfect future and explore how we might achieve it.

• Worst-case scenario: Brainstorm potential threats and challenges of the future.

Remember, the future is uncertain, but we can be better prepared to face whatever it holds. What ideas spark your curiosity?

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The things to know

• Ethics and discrimination in artificial intelligence-enabled recruitment practices on Nature

• Fairness and bias in algorithmic hiring on ARXIV

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Explore more on YouTube

• The Bias Problem With Hiring Algorithms

• Podcast: In Machines We Trust - Hired by an algorithm

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Share you thoughts

• How do you think artificial intelligence is transforming the world?
  

• Please take a moment to comment and share your thoughts.

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