The Rise of Explainable AI (XAI) in Data Science

AUGUST 13, 2022


AI has taken over the technological landscape by storm. Artificial Intelligence regulates almost everything, from using text editors to summoning Alexa. Given AI's potential, companies rapidly implement it into their business operations. However, with AI's advanced analytics comes the issue of transparency of ethics—a concern raised by professionals and experts.

Explainable AI (XAI) offers a more efficient solution to harnessing AI's capabilities while maintaining transparency.

Introduction to Explainable AI (XAI)

XAI takes AI up a notch; it describes Artificial Intelligence models and their abilities. XAI is a set of processes that makes machine learning-generated results comprehensible, reliable, and trustworthy. Explainable Artificial intelligence enables accurate and transparent characterisation of AI models, enabling confident and AI-driven decision-making. Not only does XAI increase productivity in a work environment, but it also helps organisations modify their AI approach. An example of XAI improving AI tools is the role of explainable AI in credit risk management.

As AI continues to develop, humans are under pressure to understand the algorithm from scratch and retrace the entire process. The complete algorithm calculation process, known as a 'black box,' is a direct result of data—something even data scientists, who developed the calculation, struggle with understanding. Therefore, they cannot precisely explain how AI generated the output; what was the thought process?

Explainability in AI solves the AI-caused dilemma by helping developers understand the workings of a system, including reassuring them of the system's functionality, regulatory guidelines, and how the AI-enabled framework affects decision-making or modifies outcomes.

Understanding the Importance of Transparency and Accountability in AI

Despite's AI boundless potential, data scientists worry about its ethical ramifications. Due to AI's abstract stance on fair and safe use, awareness of the need for a more responsible framework for AI technologies is rapidly increasing. Companies and scientists are considering the moral, ethical, and legal viewpoints of deploying AI-powered machines.
Advancements in machine learning allow AI systems to make decisions on behalf of humans without manual assistance or interference. While this may seem efficient and advanced, it compromises human autonomy and control. Some explainable AI examples include self-driven cars and autocorrect. Self-driven cars function without manual operation. They accelerate, drive, hit the brakes, switch lanes, reverse, and park independently. However, as advanced as it might seem, removing human control might lead to roadside accidents. Similarly, phones autocorrecting words implies that Artificial Intelligence understands the person's mind and text better than the person does, thus removing the user's autonomy from the equation. Therefore, with AI development comes the issue of control, transparency, and accountability; explainable AI principles follow the transparency model.
A call for transparency and accountability in XAI ensures that AI-led systems follow a responsible approach, exhibit reliable behaviour, and justify their actions regarding data acquisition, usage, and decision-making.

Explainability in AI: Accountability

Accountability means explaining and justifying one's actions to the people involved. Responsibility occurs when the decision-making systems successfully and correctly describe the thought process behind their actions, such as when, how, and why. In the case of AI, accountability refers to the need to adhere to ethical, moral, societal, and legal rules integral to specific operations that the AI system must abide by. Responsibility in Artificial Intelligence requires the framework to justify its guiding principle and explain its functioning.

Explainability in AI: Transparency

Transparency means clarity. It refers to the need to clarify the factors surrounding and involved in AI operations, such as the mechanisms through which Artificial Intelligence tools work, adapt to their surroundings, and use the data generated.

The Challenges and Limitations of Traditional AI Models

The impact of Artificial Intelligence on society and daily activities is unquestionable, undeniable, and unmatched. As technology advances, AI's potential will grow, seeping into the remaining corners of the community. However, like other tools and technologies, conventional AI models also have shortcomings and limitations that raise concerns and question their authority, authenticity, and reliability. The AI vs XAI debates touches upon these concerns in great detail. AI Bias One thing that Artificial Intelligence has in common with society is biases. Similar to how biases affect

One thing that Artificial Intelligence has in common with society is biases. Similar to how biases affect one's societal standing, biases in AI-powered data models and processes affect their functioning. AI bias is not only terrifying but it also inserts identified or hidden prejudices of the developers. Unlike tainted data, biased Artificial Intelligence works in multiple stages. For instance, biased AI in deep-learning processes can affect the entire framework and design guidelines; incorporating explainable AI in deep learning can rectify this.

Lack of Accountability and Transparency

Artificial Intelligence has shortcomings, including a lack of transparency and accountability. The input data can be tampered with, modified, or wiped out without visibility. A transparent AI model is necessary for data scientists or developers to track the root of the problems or identify the issue.

Profiling in Explainable AI

A frightening downside of unregulated AI practices is creating accurate user profiles without their knowledge or consent. AI algorithms identify user behaviours, such as typing patterns, log-in times, and data history, to develop their personal profile. AI models can predict a user's check-in times, actions, messages, and location based on their online activity and history. Creating profiles according to AI prediction works on an accurate and precise understanding of a user's online behaviour or interactions with their contacts or the applications on their devices.

The Benefits of Explainable AI in Data Science

XAI plays a significant role in the field of data science.

Reducing Error Margin

Incorporating XAI in data science projects reduces the margin of error, especially in fields that require accurate decision-making tools, such as healthcare, legal, education, and finance. A single wrong prediction can crumble their systems. Furthermore, oversights in result generation make it difficult to trace its cause. On the other hand,explainable AI principles minimise the scope of errors and reduce their consequences by identifying the source of the problem and improving the model. Deploying XAI helps AI models like AI bots perfectly analyse human speech to create authentic pieces that look natural and are error-free.

Explainable AI's Importance: Removing AI Bias

AI bias is an understated concern that comes with using AI tools. The developers' bias reflects in the functioning of the AI model, such as racial profiling in self-driven cars and gender prejudice in Amazon devices. XAI eliminates AI bias by explaining the model's decisions.

Regulatory Compliance

AI devices must comply with regulatory guidelines to operate freely without restrictions. Various AI models require regulatory compliance and confidence from the owner or operator to work seamlessly. Some explainable AI examples where XAI ensures AI models receive high-code assurance can be seen in autonomous vehicles, credit card readers, and medical diagnostics.

Best Practices for Implementing Explainable AI in Data Science Projects

XAI comes in handy in various data science projects, from credit risk management and scores to deep learning and decision-making. However, in addition to data science projects that involve regulations, XAI can be implemented in industries and sectors that work without regulation restrictions, such as:

  • Ensuring AI tools correctly understand the user's tone, sentiments, and intentions before producing results. Explainable AI examples that accurately analyse words' meaning include AI tools for writing resumes, profiles, and cover letters.
  • Changing results when new data input clashes with existing data may skew results and the model's performance.
  • Producing accurate diagnoses in the healthcare industry, including giving patients a detailed report on their illness, its causes, identification, and treatment.
  • Explaining a company's onboarding decisions.
  • Providing product and service recommendations to consumers through messages, emails, discounts, and deals.

The Future of Explainable AI and its Impact on the Industry

XAI has a bright future in the AI industry. Its design, features, and abilities seek to transform the technological landscape and sector positively.

Better Productivity

Explainable AI's importance lies in its ability to increase productivity. XAI models can instantly reveal mistakes and identify the room for improvement, making it efficient for ML teams to monitor and oversee AI systems conveniently. For instance, analysing specific modules of an AI model result can help developers ensure whether the model identifies user patterns and provides accurate predictions. Correct analysis reduces the margin error, increasing productivity and improving performance.

Mitigating Risks

XAI helps firms prevent and control risks. Since XAI adopts a moral and ethical approach, it ensures AI models work according to media, society, legal, and regulatory norms. Explainable AI principles include abiding by moral codes. Legal teams use XAI to ensure the system adheres to prescribed rules and guidelines and aligns with the organisation's internal goals, policies, and objectives.

Increases Brand Value

Explainable AI's importance is boosted by its ability to enhance the brand value. It allows technical and business departments to collaborate and ensure the company meets its goals timely, thus generating value and improving its brand image.

In Conclusion

Artificial Intelligence is a technological wonder designed to make daily operations more efficient and to take the digital age to new heights. However, AI's limitations hinder its functionality, such as a lack of transparency and AI bias.Explainable AI gives meaning to an AI model's functioning. It rectifies the existing problems within the AI system by making the framework clear, transparent, error-free, and unbiased.