Is Artificial Intelligence (AI) of today, another version of Human 2.0 of tomorrow?

This year has been dominated with Artificial Intelligence (AI). Individuals, organizations, community are experimenting with chatGPT and exploring ways to adopt, adapt and improve their goals.

As with advent of any modern technology it is responsibility of all of us to make sure we understand the opportunities and risks which comes with it. Before we delve into the topic of Artificial Intelligence (AI) Bias lets under what Artificial Intelligence, ChatGPT and Generative AI and finally what does Bias mean.

Artificial Intelligence (AI) is a field of computer science that focuses on creating machines that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

ChatGPT (Generative Pretrained Transformer) is a computer program that uses Artificial Intelligence to understand and respond to natural language text, just like a human would. It can answer questions, write sentences, and even have a conversation with you. ChatGPT uses a machine-learning algorithm to scan text across the internet and develop a statistical model that allows it to string words together in response to a given prompt.

Generative Artificial Intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create latest content, including audio, code, images, text, simulations, and videos.

A bias is a tendency, inclination, or prejudice toward or against something or someone. Some biases are positive and helpful—like choosing to only eat foods that are considered healthy or staying away from someone who has knowingly caused harm. But biases are often based on stereotypes, rather than actual knowledge of an individual or circumstance. Whether positive or negative, such cognitive shortcuts can result in prejudgments that lead to rash decisions or discriminatory practices. Bias represents injustice against a person or a group. A lot of existing human bias can be transferred to machines because technologies are not neutral; they are only as good, or bad, as the people who develop them.

Now, that we have a high-level understanding of what Artificial Intelligence, ChatGPT and Generative AI means lets dive in.

People, Process and Technology are the three vertices of IT ecosystem and its always important to keep the equilibrium among these three. If one vertex stretches to the limit without having other vertices balancing it out, you get what is called, a debt.

AI has the capability to change the world but its important to understand what it is using to learn was originally created by humans and as we move forward with technology how do we keep our biases out of the algorithms we create? Because once skewed data gets into deep learning machines, it is exceedingly difficult to take it out. If we do not pay attention NOW to the quality of data being fed to these algorithms, tomorrow will be too late to sort things out.

Can you imagine a just and equitable world where everyone, regardless of age, gender, or class, has access to excellent healthcare, nutritious food, and other basic human needs? But as we know it is not usually the case. As Humans we are inherently biased. Sometimes it is explicit and other times it is unconscious.

I have consolidated common bias which exists today, and, in some studies, we are seeing these biases are finding its way into AI models already.

• Cognitive bias - are repeated patterns of thinking that leads to inaccurate or unreasonable conclusions. Cognitive biases may help people make quicker decisions, but those decisions are not always accurate.
• Unconscious bias - discrimination or prejudice against a person or group that is unconscious to the person with the bias. It is dangerous because the person is unaware of the bias – whether it be on grounds of gender, race, disability, sexuality, or class.
• Sampling bias - This is a statistical problem where random data selected from the population do not reflect the distribution of the population. The sample data may be skewed towards some subset of the group.
• Temporal bias - This is based on our perception of time. We can build a machine-learning model that works well at this time but fails in the future because we did not factor in probable future changes when building the model.
• Anchoring bias - People tend to jump at the first available piece of information and unconsciously use it to “anchor” their decision-making process, even when the information is incorrect or prejudiced. This can lead to skewed judgment and poor decision-making, especially when they do not take the time to reason through their options.
• Attribution bias - This occurs when someone tries to attribute reasons or motivations to the actions of others without concrete evidence to support such assumptions.
• Confirmation bias – This refers to the brain’s tendency to search for and focus on information that supports what someone already believes, while ignoring facts that go against those beliefs, despite their relevance.
• Hindsight bias – You believe they should have anticipated certain outcomes, which might only be obvious now with the benefit of more knowledge and perspective
• Dunning-Kruger Bias - People lack the self-awareness to accurately assess their skills. They often wind up overestimating their knowledge or ability. For example, it is common to think you are smarter, kinder, or better at managing others than the average person.
• Attribution Bias - People are more likely to attribute someone else’s actions to their personality rather than considering the situation they are facing.
• Halo Bias - The Halo bias occurs when your positive first impression of someone colors your overall perception of them.
• Negativity bias - This negativity bias explains why we focus more on upsetting evens, and why the news seems so dire most of the time.
• Optimism bias - People tend to overestimate the likelihood of positive outcomes when they are in a good mood and vice versa.

I will leave you with this, there is an urgent need for everyone to have a conversation within your organizations to be more proactive in ensuring fairness and non-discrimination as we leverage AI to improve productivity and performance. There is a need for AI governance and overseeing ethical standards around what we feed to the machines.

Let us make sure we feed healthy un-biased data to these machines. "Because once skewed data gets into deep learning machines, it's very difficult to take it out."