How can you avoid bias and discrimination when using AI and machine learning for recruitment?

To avoid bias and discrimination in AI-driven recruitment, ensure the training data is diverse and representative to prevent biased outcomes. Regularly audit algorithms for fairness and transparency, adjusting them to minimize any detected biases. Implement human oversight in decision-making processes to balance AI recommendations. Additionally, maintain a commitment to ethical guidelines and continuously update the technology to align with evolving anti-discrimination standards.

No matter what algorithmic process, AI system, machine learning system we use, it's always biased with the input we train it on. Hence, it's important to provide a diverse data set. In many cases this means hand selected training material, rigorous testing of the systems and their output and retraining with necessary data to reduce biases. The point is: This look never stops, you keep on evaluating theses systems and retraining it. And maybe worth adding: One alone cannot evaluate such systems, it also needs a diverse group of people who do that, in order to avoid biases in that process.

100% accuracy in reducing bias while using AI and machine learning system seems to be difficult. But AI is the need of hour. The best approach is definitely creating a group which is diverse and can help in auditing at dynamic pace. There will be n number of hit and trials but keeping the right people in a group who can take up the challenge of removing bias & continuous auditing will defintely create a difference. Technology has always helped humans to evolve in a better way. Now is AI's turn to be a revolutionary helping hand to human kind. We will have to design & run it as per our needs and not let AI drive us.

Indeed, rigorously testing the output of your systems is essential and it's worth emphasizing on the fact that this needs to be a continuous process - never ending. As your tools and processes evolve and as you add new data to the system, it's necessary to test the output with a diverse group of people - the more diverse and the larger the better, but be careful or not falling into the trap that you introduce a bias in your testing group.

It's actually not another way, it's an essential part of the process to have a diverse group of people testing the output of your systems. The smaller the group and the less diverse, the likelihood increases, you miss a viewpoint and don't spot the bias that your system has.

Been there very long time ago (+10 years at least)... Getting transparency from AI is the biggest challenge we faced and it's still the biggest issue with AI. It's simply impossible to know why the output of such systems is the way it is, because of the massive amount of data and computation - an amount no human brain can comprehend. It's like trying to understand why a human brain (of course on a much larger scale) decides the way it decides. Of course you can ask the human, you can also ask the AI, but will it tell the (full) story? Can they even completely understand or communicate how it/they came to a specific conclusion? But yes, agree, educating the team about these tools and ensuring you have a channel for feedback open, is crucial!

The rise of AI is great - we finally have the computational power to train large models and it's amazing how quickly these systems evolved and I'm looking forward to see the next iterations. Should we be afraid of AI? I don't think so. It's going to disrupt a lot of things I guess introduce changes to our lives and the way we work, collaborate, learn and how we spend our spare time. If done right (tbd...), I think it could solve a lot of problems in this world and give us more time to do be more creative, while the boring, repeatable tasks can be done by such systems. Wouldn't that be wonderful? Shouldn't we embrace such a nod to the future of living? And yes, it could also cause harm; Therefore let's start educating our children about it!