How do you address biases in team dynamics?

The article title is about addressing biases in team dynamics but only focuses on the individual. Biases are often systemic, so it's best to take a holistic approach to identifying and addressing them. When it comes to positions of leadership, take a look at a number of factors that help identify bias. What's the composition of the team? When the team interacts, are there cliques or exclusionary activities or conversations? Who engages in/leads group discussion within the team? How are decisions made, and by whom? Who gets to work on the most prized projects? Who gets recognition and rewards (both verbal and material)? Who gets professional growth opportunities? What sort of language is used when providing feedback across the group?

- In my experience, I see bias easier to identify by continuously reflecting for 15min a day which can reveal so much of hidden engraved bias that we adapted to and is normal part of our life. - for example; bias against different citizens of different areas like Arabs and subconsciously associating with different ideologies like being a Muslim, strict, dictator, Rich or sometimes poor, not understanding English or other languages.. etc.

There's a saying in the world of neuroscience: "If you have a brain, you have a bias." We all have biases through which we see the world. Our goal should be to become conscious of the biases we hold and to prevent them from translating into discriminatory words or conduct, i.e., never treating any human being negatively because they are different from us. While unconscious bias training is not a panacea, sometimes, just getting a conversation started can create space for meaningful dialogue to address biases.

- I agree with awareness but also need to encourage and educate different teams on the impact and benefits of not being biased and how it could reflect positively on themselves

Being aware of your biases is the starting point so seek feedback from peers, colleagues etc ask uncomfortable questions and then work on your biases

Building relationships with people different than yourself is an excellent way to help combat bias, but don't treat it as a substitute for doing your own research first. There's a wealth of written and recorded information that will cover the basics (and many advanced topics) that you should use as a basis of knowledge. Show respect for people's time by not overwhelming them with easily-answered questions.

- In my experience, if it makes you uncomfortable then there is something hidden that needs to be revealed and understood and there is an engraved bias that could be hidden in the subconscious from the way we are raised at very young age. - for example; ignoring homeless people or providing a helping hand sends a serious message of bias on how to treat them

In addition to celebrating strengths and contributions of each team member, encourage the entire team to call out bias when they see it, even if it's not directed at them. The team is an envelope of support and accountability for all its members. If there are team members who consistently cause relationship problems on the team and refuse to change, remove them. Constructive conflict is good, destructive conflict is not, and will spread to others.

Having a zero-tolerance policy for discriminatory behaviour can also contribute to a safer and more inclusive team culture. For example, if someone says something inflammatory to another teammate based on a characteristic of their identity (such as their race, sex, gender identity, or religion) it's important to intervene and respectfully communicate that discriminatory conduct won't be permitted. Intervening in real time when a workplace injustice occurs—when safe to do so—can communicate team values and create opportunities for collective learning, growth, and healing.

This is very important aspect building team culture and make the team realise that they are part of the team. Also give them an opportunity to help eachother, show empathy, learn and move forward from mistakes, challenge the process not the person and focus on continuous improvement will enhance team culture.

In my experience, being clear that as a team this matters is the first step. What gets measured gets done. Whether the company has a scorecard or not, there are ways to measure. Which means you need to have a best practice and rigor around monitoring and adjusting . Seek feedback, acknowledge misses and change any behavior related to biases are the best leading indicators. When you create a stride here you will constantly evolve and grow as an individual and a team. If you are the leader, letting your team know that you want to hear what you don’t want to hear , let’s others know you don’t just want to feel good but want them to be heard. You will be modeling humility. You won’t always get it right because there is no destination.

Check your measures make sense to the people who are negatively impacted by the biases. An organisation might measure how many people in a group are recruited and how many reach management- that may not be the measures that the group experiencing bias would choose, if, perhaps, they take 5 years longer to get to management than similarly qualified colleagues or if they do so by having to take on extra projects to get the same visibility. Check your assumptions about how biases manifest and make sure you are counting what counts.

These insights on biases are indeed valuable. However, there's another perspective in the era of AI: algorithmic bias. AI systems are trained on data. If this data is biased, the resulting models will be too. In teams, AI tools used for HR tasks can inadvertently propagate biases leading to unfair treatment. Acknowledging that AI, like humans, can be biased is the first step. We must strive for transparency and fairness in AI by understanding how data is collected and how AI models make decisions. Educating our teams on AI bias is vital. Used ethically, AI can counteract human biases by identifying unseen patterns, aiding in more objective decision-making.

Understanding the problem helps to avoid bias in most of decision making, human do make dias based on certain preferences, choices.

The problem is indeed what data is collected. Actually, what data is available at all, because even about things that would seem to be pure matter of factly, the collected information is provided by humans, who have no inclination to have a "Mr. Spock pure logical" attitude for anything. If the AI has not direct access to instruments and so on, it's source will always be biased as provided by the selective minds of human beings. This is also the problem with media. Human and artificial intelligence all build on top of the results of human intelligence. So, for human and artificial intelligence the best advice is to collect information from a many different sources as possible. And THEN come to a conclusion (and keep collecting information).