- 6 min read
Responsible Artificial Intelligence in Enterprises for Customers
- Jagreet Kaur Gill
- - posted on Oct 26, 2021 9:17:43 AM
Introduction to Responsible AI in Consumer Enterprises
As Artificial Intelligence technologies become more pervasive, solutions such as recommending products, risk assessment, behavior assessment, personalization processes, etc., become less visible and transparent in customer enterprises. Because most of the AI approaches that provide accuracy have black-box functioning. Enterprises face ethical, legal, and regulatory risks using a “black box” approach to AI. For helping humans in decision-making, AI is using data to learn and know the patterns. People feel helpless when they know that the system denies their loan application without any explanation. They feel astonished when they know that their sensitive data got leaked and suspiciously that enterprises are using their data to manipulate their behavior. These issues in AI systems create distrust in consumers and slow down the adoption of AI and innovation.
We may require a solution to provide privacy, security, explainability, and ethical choices in the AI system using consumer data. It also must offer interpretable ML so that consumers know how the system is making a decision. These features can improve consumer trust in AI solutions by assuring responsible behavior of the system.
In this solution, we will discuss an approach of Responsible AI that can tackle AI applications’ major challenges. It provides features like Ethical, Secure, Human-centered, Preserving AI with data compliance and governance. After that, some of the lawful, robust, and ethical use cases of Responsible AI in Consumer Enterprise.
Lack of fairness in AI applications has become a common issue. An increase in the number of cases becomes a bottleneck for adopting AI in enterprises. Another primary reason for distrust of consumers is the black-box functioning of AI applications. Most AI applications are using Machine Learning and DL approaches for making decisions and recommendations. But these technologies are opaque; it means they are not able to justify how they make decisions. Hence consumers are not able to get to know how they work. If one could make the AI applications transparent and understandable, it could unlock innovation and some more AI applications.
What is Responsible AI In Consumer Enterprise?
Our Responsible AI in consumer Enterprise brings many practices together. It ensures the ethical, transparent, and accountable use of AI technologies for recommendations, anomaly detection, segmentation, risk assessment processes. It works in a manner consistent with consumer, societal, enterprise laws and norms. It guards against malicious activities, bias risks, and other unethical practices. Thus it brings individual and societal security and engagement.
Above the high-level architecture is given for the overall development of responsible AI. Where in each step, it is made sure to check the regulations and principles. Below another diagram is given that is used for explaining a system. Responsible AI uses a combination of tools, technologies, and different frameworks to make a responsible framework.
Why do we need Responsible AI In Consumer Enterprise?
To solve the above challenges, we must use Responsible AI that can provide a secure, reasonable, and transparent system. Let’s discuss why we need to use Responsible AI and which features it provides?
- Transparent: Interpretable ML provides a transparent algorithm. It provides a complete data, model, and system decision interpretation and justification to understand the system logic and data contribution. As a result, it makes it easy for them to identify whether the system is working correctly or not.
- Accountable: Responsible AI allows one to become accountable for the AI that an enterprise is delivering. There are some principles of Responsible AI; the use of those principles makes them accountable and answerable. Those sets of rules and regulations make their decisions deliverable and explained to others. The self-explanation capability of Explainable AI increases accountability.
- Trusted: Transparent and interpretable nature of AI systems build customer trust. It makes them identify system limitations. Hence boost innovation and adoption of AI systems.
- Governance: It is a foundation of Responsible AI that aligns the business strategy and improves the model output. IT helps to make a consistent process by tracking the system.
- Human-centered: People-first approach builds human-centered AI systems. That is for human well-being and under the control of humans.
- Secure AI: Data security of consumers is on top. Secure AI helps to build a system with proper data governance and model management. It can identify vulnerabilities and reduce attacks.
- Value of Alignment: Ethical AI allows the system to consider human morals and values. It will enable us to differentiate the right and wrong situations. Humans make decisions based on various factors such as human rights, experience, cultural norms, memories, etc. Therefore Considering the culture that establishes the value within the system is a must.
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What are the Challenges of Responsible AI in Consumer Enterprise?
Before discussing a solution, first, it is required to discuss the problem around which we want to have a solution to efficiently and quickly map them both. Here we will discuss some common challenges of AI application, for which later we will discuss a solution.
- Bias: AI systems are using historical and societal data for making decisions. For a long time, there has been some bias in society against some communities. Unintentionally that bias was also inputted to the application in the form of data. Hence, the machine learns based on the bias data; therefore, the system’s output also consists of bias. This is the major reason for biased applications; otherwise, algorithmic bias cases are also noticed. This becomes a significant challenge for AI systems.
- Privacy: The data used for the AI system most is stored digitally on a single internet. It could be hard to control access to data. The chances of theft and stealing of data also increase. Suppose that data may contain any sensitive or confidential information. In that case, it can become troublesome for them to keep them secure from hackers. No doubt many techniques are coming around the authentications, but still, data is always at risk.
- Opacity: Lack of transparency reduces the opportunities for human engagement and perception. So, consumers cannot understand system logic and limitations. Therefore it becomes difficult to build customer confidence. Thus, it is also not possible for the developer or user to track bias that increases the chances of bias in data and the system’s decision. As a result, there could be a lack of a feedback loop, risk of errors, and performance instability.
- Control: Poorly designed AI systems can have the risk of becoming rogue. It also becomes an issue of debate that malevolent AI might pose a serious threat to humankind.
- Ethical Issues: Society has some morals, values, ethics, and rights. People consider these values while making decisions, but some ethical issues are noted in AI systems. They are designed in such a way by having a specific objective in mind that may compete with overreaching societal ethics and values within which they operate. It may contain the risk of value misalignment.
- Lack of Governance: Lack of clear data governance is an issue that gives birth to various other problems such as accountability, business strategy alignment, consistency, etc.
- Evil Genie: AI can work as an evil genie that will obey the order, but the consequences of the way it uses to obey the order can be terrible. But this is only when there is a lack of understanding of full context when the system trains.
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How does Responsible AI In Consumer Enterprise work?
Let’s discuss how it will work:
As shown in Figure 1.4, the Model and data are given as the input to Explainable AI libraries, and then it is checked how efficient the model is.
- Collect ML models and data.
- For AI Fairness, we use some explainable AI frameworks and libraries to check bias and data quality to track the data’s health.
- Global and Local interpretation of the model performance and decisions will be provided using some Explainable
- AI methodologies such as LIME( Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive explanations).
- Results generated by the methodologies will be displayed using visualization to be easy to understand and check whether the model is accurate.
Use Cases of Responsible AI In Consumer Enterprise
- Recommendation Systems: It compares the consumer action based on their activity, interest, and preference, then infer similar tastes and suggest an affinity between consumers. Based on that, it recommends the product, service, or whatever thing the consumer is looking for. E-commerce websites use consumers’ actions and data to know their interests and preferences and then help them recommend products they are looking for.
- Audience Segmentation: Segregate consumers into groups according to their similar behavior and nature for marketing and product performance. It analyzes customer data to create more targeted segments for the marketing strategy. It automatically adjusts campaigns that are more personalized for each campaign. Responsible AI makes sure that only written campaigns can be used for the right people only.
- Personalization: Modify the content and experience of marketing, products, or channels to best resonate with consumers at a scale. The consumer creates helpful content of posts, blogs, videos, products, etc., to increase consumer engagement, loyalty, and conversion. It provides the things that might be enjoyed by the consumer based on the past interaction of consumers. IT helps to increase brand utility.
- Chatbots: Chatbot works as an assistant for the consumer to make their journey easy and successful. It answers the consumer’s questions, resolves their queries, and provides them with the right product.
- Risk Assessments: It can be used in the bank or insurance industry for modifying and offering prices based on the predicted risk. It allows checking the probability of a person becoming a default or risk associated while giving them a loan or insurance. Based on that detail, it suggests the interest and price amount.
- Anomaly Detection: Identify the customer behavior and patterns to track anomalies to reduce the customer churn rate. It can also measure the network’s behavior to check the anomaly or any malicious activity in the network. Responsible AI would help to determine whether there is an anomaly or not.
- Data Products: To increase revenue and improve marketing strategies, use algorithms to find the customers’ useful insights.
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