What is Explainable AI and why is it important? 

Traditional Black Box AI systems automate decision making and offer limited visibility into how the algorithms work. In a time, when transparency is everything, can we really trust artificial intelligence systems? In this article, we explore the concept of AI Bias and the role of Explainable AI in eliminating AI Bias and increasing model transparency

What is AI Bias?

AI bias is defined as “a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.” This happens when AI models ingest societal biases leading to flawed outcomes. The examples are many: Microsoft’s bot Tay learning racial slurs and Twitter’s photo cropping algorithm blotting out African people.

Why it is important to eliminate AI Bias?

Without a way to check these biases, AI models grapple with inefficiencies. Model accuracy comes under scrutiny, leaving users feeling distrustful about model recommendations. The effectiveness of model predictions also suffers because of results that reflect a skewed reality. For instance, biases in automated loan underwriting can unknowingly isolate an entire demographic of customers that are eligible for affordable loans, leading to negative brand image and lower profitability.

Biased model outcomes also inadvertently encourage discrimination. Seeking to mechanize recruiting, Amazon designed a machine learning program, AMZN.O. It was later found that the algorithm was rating candidates in a non-gender-neutral manner, heavily preferring men over women. On deeper investigation, the fault lay with one of the datasets that used resumes submitted to the company over a period of time, most of which were from male candidates. AI biases can breed a lack of accountability in decision-making within the organization, compromising an open and transparent culture. To gain user trust, AI systems need to be responsible and free of bias. Explainable AI plays a vital role in eliminating model bias and improving AI Adoption.

What is Explainable AI and why it matters?

Explainable AI deals with the concept of building transparent AI systems. According to Google, Explainable AI is “a set of tools and frameworks to help enterprises understand and interpret predictions made by machine learning models.” It is used to describe an AI model, the expected impact, and potential biases. It debugs the model and gives users insights into model behaviour to improve performance.

But perhaps one of the most pioneering features of Explainable AI is that it can resolve biases and gaps within AI models. Simply put, Explainable AI allows users to understand the path that an IT system or algorithm takes to make a decision. Being a new technology with unprecedented potential to transform business and human experiences, explainable AI is critical to gain user trust and enhance AI adoption.

How does Explainable AI work?

At a fundamental level, Explainable AI involves exposing the logic within black box models – and thereby any fallacies – used to drive AI outcomes. A black box model is a catch-all term used to describe a computer program designed to transform various data into useful strategies. In machine learning, these black box models are created directly from data by an algorithm, meaning that humans, even those who design them, cannot understand how variables are being combined to make predictions. The differentiator, therefore, is transparency. When AI models are made transparent, it instantly provides scope to correct human biases.

Best practices to leverage Explainable AI and eliminate AI bias

As the evidence suggests, AI models can embed societal biases and deploy them at scale. Feeding such biases is typically unintentional. Nevertheless, to stay ahead of risk, enterprises need ways to purposefully make AI accountable. The goal is to convert the art of making AI responsible into the science of making AI explainable.

1) Remove bias in the underlying data

Datasets used by AI systems are often the root source of bias. Biases in datasets occur due to two broad reasons. One is the lack of sufficient variety and distribution of data. For instance, non-representative methods of collecting, sampling, and selecting data for the model. Two, decision biases may sprout from past recorded human decisions based on flawed assumptions or societal/historical inequalities. To avoid such biases becoming part of the underlying data, one must proactively ensure that datasets with adequate representation are being used. Platforms that offer a range of granular visualizations assist data scientists in decrypting patterns, ensuring representative samples, and assessing whether the input data is skewed or not.

2) Weed out socially or legally unacceptable correlations

Sensitive variables such as gender, ethnicity, and race are often intentionally avoided as inputs in algorithms. However, these can be picked up from other correlated variables. For example, AI systems may derive ethnicity from geographical locations, and age from the number of times a service has been used. Data scientists must be primed, therefore, and take extra effort to identify such possibilities beforehand. Dashboards that provide visual representations of data and its correlations can greatly help data analysts understand algorithmic logic. These also allow data scientists to apply their own understanding and domain skills to debias the model.

3) Integrate user feedback to ensure model improvements

An AI model should mandatorily include feedback from end users about how the model functions in the real world. This requires steady, continuous, and persistent model testing to refine the model for greater levels of accuracy.

How HyperSense Explainable AI helps eliminate AI Bias?

Companies are wary of the implicit risk within AI models, and want solutions that help them mitigate potential negative impact. HyperSense AI Studio is one such solution. HyperSense AI Studio comes with explainable AI capabilities. It eliminates bias due to costly errors and ensures transparency in prediction, thereby improving model performance and building user trust through AI trust and governance framework. It enables organizations to build trust and confidence when putting AI models into production. It also improves accuracy and fairness when interpreting the models and algorithms.

Key takeaway

Building a system of trust within the AI landscape calls for well-formed ethics, governance, and frameworks. The definition of ‘AI trust’ must be deconstructed and every element transformed into a metric that is measurable and transparent. To ensure unbiased, transparent, and trustworthy results, enterprises need to interpret AI models and their predictions with Explainable AI capabilities with solutions such as HyperSense AI Studio.

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