AI has permeated every part of our lives, evolving from recognizing patterns to achieving human efficiency. Treading deeper into the AI landscape, generative AI (GenAI) has become the new normal, reshaping every industry. Research estimates that GenAI could contribute between $2.6 trillion and $4.4 trillion to the economy annually while accelerating the impact of all artificial intelligence by 15% to 40%. In the next three years, businesses that shy away from GenAI and AI could be perceived as outdated.
Despite the opportunities, there’s still apprehension surrounding the associated risks of GenAI adoption. Unlike AI, GenAI typically leverages any available data types or formats to generate results. This might be useful but also very unreliable. A recent survey indicates that 20% of global business and cyber leaders cited data leaks and exposure of personally identifiable information through GenAI as a top concern.
How can we mitigate the AI trust issue? By ensuring transparency across the AI process, eliminating bias, and clarifying how AI reached its decision — through explainable AI (XAI).
GenAI’s meteoric rise
As highlighted earlier, GenAI, while still in the nascent stage, has carved out a niche for itself.
Continuously evolving, it has broadened the range of utility from building customized models to generating creative ideas, engaging in context-aware conversations, and producing images and videos based on text commands. Its impact extends beyond any single industry. For instance, in insurance, GenAI can help underwrite policies by analyzing vast amounts of structured and unstructured data to identify patterns and predict risk. In retail, GenAI is used to make personalized product recommendations — by analyzing customers’ past purchases, preferred brands, and even social media activity. At the same time, retailers can use this information for more effective and targeted customer retention.
Additionally, businesses have witnessed increased efficiency, automation of trivial tasks, minimization of labor costs, and overall transformation in their interactions with GenAI. To illustrate better, a North American life insurance provider digitized its core systems with GenAI, improving the response time and delivering exceptional customer experience.
Hence, companies increasingly rely on GenAI to make vital decisions, pushing for better ways to convey how deep learning and neural networks work. When users, regulators, and stakeholders understand the ‘why’ behind GenAI’s insights, it fosters a sense of trust. This is achieved through innovative tools and processes that simplify the explainability of predictive insights.
Significance of XAI between user trust and GenAI
XAI assists human users in understanding the reasons and purpose of GenAI, encouraging responsible usage. As businesses shift from black-box processes to transparent white-box models, they deepen trust between GenAI models and users while fostering continued innovation. This highlights the importance of bridging the gap between GenAI workings and its users through XAI in several areas:
- Trust and verification: GenAI models can generate falsified answers that seem correct, known as AI hallucinations. XAI can explain the rationale behind GenAI systems’ content, accelerating the process of rectifying nonsensical content, strengthening users’ faith in GenAI applications, and building GenAI systems grounded in reality.
- Security and ethics: XAI is elemental in securing GenAI systems against manipulative attacks and ensuring they adhere to ethical standards. If GenAI commits a mistake, it can attract the public, media, and regulators’ attention. Legal and risk teams can then use explanations from the technical team to ensure GenAI follows the law and company rules.
- Interactivity and user control: Creating user-friendly XAI is indispensable yet a huge challenge with GenAI models operating as black boxes. Advanced XAI techniques enhance user interactivity by enabling users to question the rationale behind AI-generated content, request a different answer by fine-tuning the command, and receive tailored clarifications based on their explainability needs.
Modernizing XAI with breakthrough technologies
GenAI can transform the world, enhancing users’ lives through personalization, accurate responses, and creative outputs, allowing human creators to focus on more important tasks. However, understanding the mechanics of GenAI is currently a complex task. Explainability can be augmented through the following methods:
- Interactive XAI: Employing dynamic and interactive explanations empowers users to receive real-time explanations and adjust further queries based on insights generated. For example, XAI can enhance product quality, streamline production, and reduce costs by identifying factors that impact product quality in manufacturing. With accurate reasoning behind these factors, manufacturers can evaluate their processes and decide whether to implement XAI’s suggestions.
- Visualization and attention mechanisms: These techniques map and visualize the influence of input data on GenAI outputs while offering intuitive and accessible explanations. A case in point is a group of data scientists in Europe who built a prototype of an XAI visualization platform that encourages the human understanding of graph neural networks (GNN). This is achieved through visualizing patient-specific networks, relevance value for genes, and interaction using the XAI method GNNExplainer.
- Scenario-based design: Tailoring XAI functionalities to specific applications and contexts improves the relevance and efficacy of explanations, making them more user-centric. For instance, a study focused on code translation, autocompletion, and natural language to code created a software engineer persona named Alex. Alex and his teammates interacted with a GenAI system to request new information and additional functions. The study observed a preference for natural language explanations with GenAI and noticed that explainability needs to vary based on the team members’ programming level.
Navigating the challenges of implementation XAI
How can enterprises reap the XAI benefits despite challenges?
The lack of interpretability of GenAI presents serious obstacles to achieving fair and bias-free content. Another challenge lies in enhancing machine learning (ML) capabilities to excel in tasks, given the continuous influx of data. However, these challenges can be mitigated with mindful and effective implementation of XAI, such as:
- Complex models: GenAI models rooted in deep learning are intrinsically complex, making it challenging to extract comprehensible explanations. Techniques like layer-wise relevance propagation, feature visualization, and saliency maps try to uncover how the model works.
- Balancing performance and transparency: Often there is a trade-off between the performance of GenAI models and their explainability. High-performing GenAI systems are less interpretable, complicating efforts to ensure transparency. Researchers need to find a balance between a powerful model and interpretable decisions.
- Diverse and dynamic requirements: Different industries and applications need different levels of explainability. Regulations, ethics, and user trust push for the creation of flexible XAI solutions. More importantly, XAI techniques must adapt to diverse industry applications and settings, from healthcare to retail and insurance.
As GenAI advances and becomes more integrated into human activities, research and collaboration among researchers, developers, and stakeholders become essential for tackling its complex challenges and explainability. Moreover, incorporating explainability concepts into GenAI applications and standardizing metrics to measure explainability contribute significantly to overcoming these challenges.
The future outlook of XAI
The XAI market is expected to reach USD 16.2 billion by 2028, with a CAGR of 20.9%. GenAI offers endless possibilities and will be utilized across industries to drive innovation, expedite time-to-market, and gain a competitive edge. The substantial growth of GenAI has spurred the necessity for regulation. It is anticipated that within the next two years, 50% of governments will advocate for responsible AI.
Achieving this requires fostering collaborative explanations, where various GenAI applications collaborate to enhance their explanatory capabilities, leading to more accurate insights. This collaborative and community-driven approach nurtures versatile cognitive solutions, indicating a promising outlook for Gen AI that delivers trustworthy, bias-free, and transparent content
AUTHOR
Sudheer Kotagiri
Global Head of Architecture and AI Platforms
SUBJECT TAGS
#generativeAI
#ArtificialIntelligence
#MAGE
#XAI
#EthicalAI
#Explainable AI