Artificial Intelligence (AI) is beyond making our lifestyles better through better movie recommendations, restaurant suggestions, resolving conflicts through chatbots, and more. The power, potential, and capabilities of AI are increasingly being put to good use across industries and in areas that nobody probably thought of. In fact, AI is being explored and implemented in areas such as healthcare, criminal justice, surveillance, hiring, fixing wage gaps, and more.
With such complex use cases, what becomes inevitable is the fairness associated in the entire process or ecosystem.
As humans, we resort to machines for answers, solutions, and information because of their objective responses. We know for a fact that machines wouldn’t take sides and offer us insights that would empower us to make better decisions or arrive at conclusions. However, the fairness at which Machine Learning (ML) and AI algorithms operate all boil down to the way they are trained.
In this eye-opening post, we explore the possibilities of artificial intelligence taking sides and delivering prejudiced results and understand if we could tackle such instances. Known as ‘bias’ in data science, this post is all about developing generic and universal ML algorithms that are fair in all aspects.
Let’s get started.
If you ask your friend how a particular movie was, chances are highly likely that they would offer an opinion based on their tastes and preferences, intellectual inclinations, life experiences, personal influences, and more. Instead of offering you objective insights on what the movie was all about, its good and bad aspects, and more, they would suggest if you should watch it or skip it. Though their opinions could be precise, they all stem from bias.
While bias from people is fine, machines need to be completely airtight in their processing, training, and output deliveries. If this sounds too complex, understand that variance and bias in AI systems are anomalies that stem out of prejudices and assumptions and completely skew results.
Bias is introduced mostly during training phases, where (either on purpose or unknowingly) AI experts feed volumes after volumes of data with certain inclinations and preferences. Such preferences, when they are introduced to AI models, influence the algorithms to make similar decisions as well.
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For instance, the recruitment AI in Amazon received flakes online after involuntarily preferring male applications over that of women. This happened mainly due to the introduction of bias when training the model with sample data sets, which in this case was of Amazon’s in-house data that stemmed mostly from a male-dominated workforce.
As AI or data science experts, one needs to be extremely careful in detecting bias and prejudices in systems and eliminating them for airtight results.
To give you a better idea, there are two major types of biases –
- cognitive bias, which refers to the feelings or judgments towards a particular person or a group depending on how they are perceived in the society
- biases that arise due to lack of data, where the unavailability of adequate data puts the AI model at the risk of getting introduced to biases as it is no longer exposed to situations or scenarios that could negate pre-existing perceptions or insights on something
With the introduction of such prejudices and opinions, artificial intelligence models become biased or inclined towards a particular race, gender, institution, school of thought, or anything that primarily needs to be all-inclusive.
Like we mentioned, bias in AI could be introduced involuntarily or voluntarily. Without going into these aspects, let’s understand that bias could stem from any of the three sources or sometimes all the three.
The results delivered by an AI model are only as effective and precise as the data it is fed. During AI training, the data that is used to train the algorithm plays a crucial role in making the model bias-free. The data should be of adequate sample size, represent diverse real-world scenarios and be devoid of any underlying social and personal prejudices.
Machines don’t have the power or the ability to validate datasets for their fairness and will learn from whatever they are fed. That’s why it becomes extremely important for data scientists to be aware of the data they use to train models. With AI influencing home loans and claims disbursal, bias could deprive people from getting loans if a system is biased. The worst part is that such scenarios would also go unnoticed in real life as audits hardly happen to detect and fix such concerns.
Perhaps a major portion of the bias stems from people. To decide what is fair and what isn’t, there needs to be a generic or rather bias-free approach in the decision-making process as well. Who decides what is fair and what is biased? What are the parameters for this analysis? Such intricate questions need to be answered and that’s why companies should bring in experts from diverse fields and enable a workplace environment that works on data and results contextuality. While accurate results are inevitable, what we also need are results that are objective and goal-oriented.
Like we mentioned, algorithms don’t have the capability to introduce new bias but when there’s an existing one in the system, algorithms could blow them up to large proportions. If a model is trained with images that show only women in kitchens and men in garages, it would equate women with kitchens and men with garages, skewing any future results. To avoid this, the models need to have instructions that dictate them to avoid such information or limit results to specific search volumes.
The good news is yes. AI systems can be free of bias and prejudices and that boils down to how clean your datasets are and how cautious you as a trainer are when it comes to voluntarily and involuntarily introducing biases into your AI system. However, there is a bad news as well.
We don’t see AI systems becoming completely bias-free anytime soon because, in the end, it’s the people who create algorithms and datasets, and to completely eliminate all instances of biases, we need to work on an ongoing basis to decrease human biases, starting from our thought processes.
What we could do instead is come up with various validations and tests to test results and bring in protocols and best practices when training AI models.
To build on the previous point further, we have compiled a list of ways to eliminate bias from your AI and ML models. Let’s look at them individually.
So, now you know three sources that give rise to bias in your AI and ML models. With this information in hand, the first thing you need to do is extensively study your datasets and understand the different types of bias that could probably seep into your system. You then need to assess its impact and make sure there is no bias stemming from data capture or data observation. When you are using historical data to train your models, you also need to ensure the data you’re using is without any preexisting prejudice. With these factors, you can eliminate bias to significant extents.
Representative data should be all-inclusive and this is the primary responsibility of a data scientist to collate data that is in line with a problem, its market segment, the population that would use it to solve real-world conflicts, and more.
Bias is often introduced into models during data cleansing stages when data is being selected from a pool of datasets. To avoid this mistake, businesses should ensure there is proper documentation of their data cleansing and selection processes. With adequate documentation, third-parties or other
stakeholders can come with their fresh pair of eyes and detect the chances of bias seeping in. This paves way for transparency and improves AI models.
Model training does not stop in labs with training data. Real concerns arise when models are exposed to the world when they are solving real-world conflicts. With there being differences in the way a model performs in labs with training data and their performance with real data, experts should consistently monitor model performance and optimize it for efficiency. This means continuously detecting bias and eliminating them. This will eliminate organizations fetching bad reputation because of skewed results.
With AI dominating our everyday lives, we are at that point in tech evolution where we need to be careful with our inputs and the models we build. Also, with everything boiling down to the quality of data training, we recommend getting in touch with us for quality datasets for your AI training purposes. We have data science veterans working in our teams, who take care of removing biases and paving the way for an objective data training process.
Get in touch with us to learn more about our offerings.
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is a CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.