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The Significance and Necessity of AI Explainability

The impact of artificial intelligence (AI) on businesses and everyday life is widely acknowledged. AI has enabled organisations to enhance productivity, reduce costs, and expand their perspectives. However, it remains crucial to maintain human oversight and control. Beyond utilising machine learning (ML) and automated decision-making, ensuring favourable results and understanding predictions requires delving into the underlying data that informs those predictions and choices. In essence, achieving truly optimised AI demands explainability.

Explainability offers insights into the rationale behind automated outcomes. AI platforms that offer complete transparency empower domain experts, data scientists, and technical personnel to genuinely grasp its dynamic capabilities. Here are some compelling reasons to incorporate explainability into your AI strategies:

Missing Valuable Insights

Neglecting to explore beyond the surface of predictions might mean missing valuable insights. While machine learning can accurately identify potential customer churn, understanding the reasons behind their departure can be immensely enlightening. Will a minor adjustment in user experience create a substantial impact? What timely incentive could persuade them to stay? Could modifying a single rule parameter significantly enhance conversion rates? Certainty in these matters arises only when you have a true understanding. Systems that facilitate easy exploration and visualisation of extensive data unveil fresh insights into interconnected factors that might otherwise remain hidden. Beyond preserving your interests, AI explainability can also lead to innovative methods of maximising benefits.

Preventing Skewed Outcomes

No system is entirely free from bias. Bias infiltrates machine learning in unexpected, counterintuitive ways. An example is Amazon's abandonment of an AI algorithm for screening job applicants in 2017, as it was found to unfairly disadvantage female candidates due to historical biases in the company's engineering hires. Human oversight is essential to prevent skewed outcomes. Furthermore, the inadvertent introduction of harmful biases into automated systems often stems from unintended human influences, primarily the selection of data used to train these systems.

Simply deploying decision logic without scrutiny is not enough, as numerous organisations that based their rule logic on biased machine learning have painfully realised.

Keeping up to date with Regulatory Changes

Anticipated laws and regulations will likely demand AI transparency. Today's legislators and regulatory bodies are well-versed in the digital landscape. As AI's prevalence grows, so does public concern and subsequently, regulatory attention. Proposals for laws mandating AI transparency and disclosing decision criteria are under consideration in both the United States and the European Union. Establishing comprehensive explainability prepares organisations to proactively address upcoming regulatory changes.

Earning and Maintaining Trust

Earning consumer trust is challenging and, for many, regaining it once lost is nearly impossible. Trust ranks higher than even a company's brand reputation. Most businesses, including large ones, would be severely impacted by negative publicity.

Trust also holds immense importance in technology. A recent survey revealed that 48% of college graduates distrust AI. However, explainability provides a means for users to have confidence in AI, its predictions, and its influence on decisions. Users can examine the factors that influenced each prediction and choice.

Given that machines are increasingly assuming responsibility for decisions, leveraging AI explainability to support human oversight will become essential. Transparent AI decisions benefit everyone – customers, regulators, and especially the organisations deploying the technology. In both business and life, trust is a precious commodity.

Gaining Recommendations

AI not only tracks and reports but also recommends actions for key performance indicators (KPIs). When determining KPIs and subsequent actions, data-driven predictions stand out. Machine learning can suggest actions based on KPIs and present the underlying data visually. ML systems reveal the data behind the numbers, making complex datasets understandable through clear visuals. Through ongoing tracking, AI-powered systems facilitate a continuous feedback loop, updating predictions in real-time.

Meeting corporate governance and public reporting obligations may not be exciting, unless you possess a user-accessible automation system capable of instantly submitting accurate filings wherever required. Accurate reporting demands the precision that comprehensive AI with Process Automation can provide. Soon, organisations not harnessing AI for reporting, tracking, and KPI actions will lag behind competitors who do.

Quicker Return on Investment

Trust leads to adoption, resulting in quicker returns on investment. Investing in AI transparency and bias mitigation is not only ethically sound, but also good for business. According to McKinsey, companies with the highest AI returns (at least 20% of earnings attributed to AI) are most likely to incorporate some form of explainability. Organisations that establish digital trust through methods like AI explainability can increase profitability by up to ten percent or more. Explainability empowers humans to identify the sources of negative outcomes and make corrective adjustments, fostering a positive feedback loop.

Undoubtedly, the push for AI transparency and explainability will intensify. Consumer demand, regulatory oversight, reporting advantages, and proven financial benefits collectively drive the adoption of automated systems equipped with robust, accessible explainability.

Our partner, InRule, was at the forefront of offering explainable decision-making and machine learning. Today, their comprehensive, user-friendly platform for explainable Decisioning, Machine Learning, and Process Automation plays a vital role in the AI-driven initiatives of prominent players in industries such as mortgage lending, insurance, government agencies, aviation, corrections, pharmaceuticals, and specialty retail, among an expanding user base.

For further details or assistance before you embark on your journey powered by explainable AI, reach out to Solentive and request a free demonstration today.

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