Overview of Responsible AI
Enterprises, businesses, the government sector, and workers continuously explore new ways to remain operational during the COVID-19 pandemic. Nationwide lockdowns, stay-at-home related orders, border closures, and several other measures taken at various levels to fight viruses have made the working environment more complicated than before.
Businesses are moving towards Artificial Intelligence (AI) based technology-oriented solutions and data to formulate processes that can function efficiently. The government started the process of facial recognition through cameras to identify and track people who are travelling from a virus-affected area. In some countries, police use drones to impose stay-at-home orders for patrolling and broadcasting important information. At airports and railways, AI-based face mask detection systems are implemented that raise the alarm to concerned departments if they detect a person without a face mask. It isn't easy to track social distancing manually in malls, restaurants, or other crowded places. The government imposed AI-based systems that continuously monitor the real-time status of buildings and raise alerts if any zone needs special attention.
AI is the reproduction of intelligent human processes, especially machines and computer systems. Taken From Article, Artificial Intelligence Adoption Best Practices
What are the Principles of Responsible AI?
The eight principles should be followed to make AI responsible and support technologies while designing, developing or managing systems that learn from data.
Human Augmentation
When we introduce AI to automate human tasks using machine learning systems, we should consider the impact that occurs due to wrong predictions in end-to-end automation. Developers and analysts should understand the outcomes of incorrect predictions, especially when automating critical processes that can impact human lives (e.g., Finance, health, transport, etc.).
Bias Evaluation
When building AI-enabled systems that have to make crucial decisions, there is always a chance of bias, i.e., computational and societal bias in data. It is not possible to avoid bias issues in the data. Technologists should document and mitigate bias issues instead of embedding ethics directly into the algorithms. They should focus on documenting the inherent bias in the data and features while building processes and methods to identify features and inference results to implement the right procedures to lessen potential risks.
Explainability by Justification
With the hype about using Machine learning and deep learning models, developers usually put large amounts of data into ML pipelines without understanding how the pipelines will work internally. Technologists should continuously improve processes to explain the predicted results based on the features and models chosen. The accuracy may sometimes decrease, but the processes' transparency and explainability help make significant decisions.
The implementation of ethics is crucial for AI systems to provide safety guidelines that can prevent existential risks for humanity. Click to explore our, Ethics of Artificial Intelligence
Reproducible Operations
Machine learning systems at production can't diagnose the situation when something terrible happens and respond effectively with a model. In production systems, performing standard procedures, such as reverting a Machine learning model to a previous version or reproducing an input to debug a specific functionality, is vital. Developers should use the best practices in the tools and processes of machine learning operations. The reproducibility of machine learning systems helps archive data at each step of end-to-end pipelines.
Displacement Strategy
When the organization starts using automation in tasks using AI systems, the impact will be vigilant at the industry level and on multiple individuals or workers. Technologists should support the necessary stakeholders in developing a change management strategy by identifying and documenting relevant information. Developers should use best practices to structure and implement related documents.
Practical Accuracy
When building systems using machine learning capabilities, it is necessary to understand the business requirement accurately to assess accuracy and align cost-metric functions with domain-specific applications.
Trust by Privacy
When industries automate work at a large scale, many stakeholders may be affected directly and indirectly. Building trust among stakeholders is possible by informing them about the data being held and explaining the process and the requirements to protect the data. Technologists should implement privacy at all levels to build trust among users and relevant stakeholders.
Data Risk Awareness
The rise in Autonomous decision-making systems also opens the doors to new potential security breaches. 70% of security breaches occur only due to human error instead of actual hacks, e.g., accidentally sending essential data to someone via mail.
Technologists should address security risks by establishing data-related processes, educating personnel, and assessing the implications of ML backdoors.
Ethical issues available in the system can only be recognized if data and algorithms are fully understood. Discover about Real-Life Ethical Issues of Artificial Intelligence
AI Adoption will not work under these circumstances
Some of the scenarios where AI is not able to respond appropriately.
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Google's facial detection system is tagging black people as gorillas.
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Models trained on Google News conclude that "a man is meant to be a programmer, and a woman is meant to be a homemaker.
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Image recognition models are being trained on a dataset related to stock photos, where most images are related to women working in the kitchen. When an image of a man comes into a kitchen to identify, it predicts the man also as a woman.
When misused or behaved abruptly, such data-driven approaches might harm human rights. There should be a sense of responsibility in data-driven strategies. To make AI responsible, adopting ethical principles and proper planning is essential. It will ensure AI-based models are against the use of biased data or algorithms and give decisions or insights that are justified and explainable, along with the maintenance of user's trust and individual privacy.
"Why should I trust AI?" For instance, if an AI-based disease diagnosis system uses a neural network to help the doctor diagnose the disease, a doctor can't go to a patient and say, "Oh, so sorry, you got cancer." The patient will ask, "How do you know?" And the doctor here can't say, "I don't know, the AI system told me so." It doesn't quite work that way. The AI system should be liable to provide some explanation related to the outcome to the doctor.
Is Responsible AI compatible with Business?
Responsible Artificial Intelligence brings many practices together in AI systems, making them more reasonable and trustworthy. It makes it possible to use transparent, accountable, and ethical AI technologies consistently with user expectations, values, and societal laws. It keeps the system safe against bias and data stealing.
End-users want a service that can solve their issues and accomplish objectives. They want peace of mind, knowing the system is not unknowingly biased against a particular community or group. Moreover, they want to protect their data according to the laws from theft and exposure. Meanwhile, businesses are exploring AI opportunities and educating themselves about public risk.
Adopting Responsible Artificial Intelligence is also a big challenge for businesses and organizations. It is usually mentioned that Responsible AI is incompatible with the business. We will discuss why Responsible Artificial Intelligence is said to be incompatible with business. Let's discuss them:
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There is a broad agreement on the principles of responsible artificial intelligence, which helps to understand how to implement them. However, many organizations are still unaware of how to implement them effectively.
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Many people think these are only verbal things that need to be discussed; they think of AI Ethics because they don't have a clear visibility of the solution, as it is a new term and has not matured yet.
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It isn't easy to convince stakeholders and investors to invest in this technology as a new term. They cannot see how a machine can fully act as a human while making decisions.
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So, the business thinks Responsible Artificial Intelligence slows innovation by wasting time convincing people and giving them a vision of why this is required and how it is possible.
An approach of proper governance, transparency, and a thoughtfully conceived process based on AI decision-making responsibilities. Source: Responsible Artificial Intelligence in Government
What are the Responsible AI Adoption Challenges?
Some key challenges that need to be addressed for the successful adoption of AI:-
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Explainability and Transparency: If AI systems are opaque and unable to explain why or how specific results are generated, this lack of transparency and explainability will threaten Trust in the system.
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Personal and Public Safety: Using autonomous systems such as self-driving cars on roads and robots could harm humans. How can we assure human safety?
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Automation and Human Control: If AI systems can generate Trust and support humans in tasks and offload their work. There will be a risk of threatening our knowledge related to those skills. This will make it more complex to check these systems' reliability, correctness and results and make human intervention impossible. How do we ensure human control of AI systems?
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Bias and Discrimination: Even if AI-based systems work neutrally, they will give insights into whatever data they are trained on. Therefore, they can be affected by human and cognitive bias and incomplete training data sets. How can we ensure that AI systems do not discriminate in unintended ways?
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Accountability and Regulation: With the increase of AI-driven systems in almost every industry, expectations around responsibility and liability will also increase. Who will be responsible for the use and misuse of AI systems?
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Security and Privacy: AI systems must access vast amounts of data to identify patterns and predict results beyond human capabilities. There is a risk that people's privacy could be breached. How do we ensure the data we use to train AI models is secure?
How can businesses successfully deploy Responsible AI?
How can a business implement AI at scale while reducing risks? To transform your business into an ethical AI-driven one, it would be best to undertake significant organisational reform.
We provide the following procedure as a starting point to assist in navigating that change:
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Define responsible AI for your business: Executives must define what constitutes appropriate use of AI for their company through a collaborative approach that involves board members, executives, and senior managers from across divisions to ensure that the entire organization is moving in the same direction. This may be a collection of rules that direct the creation and application of AI services or goods. Such principles should be organized around a practical reflection on how AI can add value to the organization and what risks (such as increased polarisation in public discourse, brand reputation, team member safety, and unfair customer outcomes) must be mitigated along the way.
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Develop organizational skills: Developing and implementing reliable AI systems must be company-wide. Driving the adoption of responsible AI practices calls for thorough planning, cross-functional and coordinated execution, staff training, and sizable resource investment. Companies could establish an internal "Centre of AI Excellence" to test these initiatives, focusing their efforts on two essential tasks: adoption and training.
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Promote inter-functional collaboration: Because risks are highly contextual, different company departments perceive them differently. To create a sound risk prioritization plan, include complementary viewpoints from diverse departments while building your strategy. As a result, there will be fewer "blind spots" among top management, and your employees will be more supportive of the implementation.
Hazards must also be managed while the system operates because learning systems lead to unexpected behaviours. Risk and compliance officers will manage close cross-functional cooperation, which is essential for devising and executing efficient solutions in this situation. -
Use more comprehensive performance metrics: AI systems are frequently evaluated in the industry based on their average performance on benchmark datasets. However, AI experts agree that this approach is relatively limited and that alternatives are actively sought. We advocate a more comprehensive strategy in which businesses regularly monitor and evaluate their systems' behaviour in light of their ethical AI standards.
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Establish boundaries for responsibility: If the proper lines of accountability are not established, having the proper training and resources will not be sufficient to bring about a sustainable transformation. The two possible solutions can be:
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Implement a vetting procedure, either as part of your AI products' pre-launch assessment or separately from it. Map out the duties and responsibilities of each team involved in this vetting process in an organizational framework. Use an escalation method when/if there is a persistent disagreement between the product and privacy managers.
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Second, employees who have reported problematic use cases and tried to implement corrective steps should be recognized as part of their annual performance evaluation.
Businesses should welcome this change since it will define who is worth doing business with.
ModelOps (or AI model operationalization) is focused primarily on the governance and life cycle management of a wide range of operationalized artificial intelligence. Click to explore our, Deep Learning: Guide with Challenges and Solutions
What are the Benefits of Responsible AI?
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Minimizing Bias in AI Models: Implementing responsible AI can ensure that AI models, algorithms, and the underlying data used to build AI models are unbiased and representative. This can ensure better results and reduce data and model drift. From an ethical and legal point of view, this can minimize the harm to users who can be otherwise affected by a biased AI model’s results.
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AI Transparency and Democratization: Responsible AI enhances model transparency and explainability. This builds and promotes trust among organizations and their customers and enables the democratization of AI for both enterprises and users.
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Creating Opportunities: Responsible AI empowers developers and users to raise doubts and concerns with AI systems and provides opportunities to develop and implement ethically sound AI solutions.
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Privacy Protection and Data Security: Responsible AI prioritizes privacy and data security to ensure that personal or sensitive data can never be used in unethical, irresponsible, or illegal activity.
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Risk Mitigation: Responsible AI can mitigate risk by outlining ethical and legal boundaries for AI systems that can benefit stakeholders, employees, and society.
Self-driving cars main goal is to provide a better user experience and safety rules and regulations. Click to explore our, Role of Edge AI in Automotive Industry
The best practices for Responsible AI
AI solutions should be designed to have a human-centric approach. Appropriate disclosure should be provided to users.
Proper testing should precede model deployment. Developers must account for a diverse set of users and multiple use-case scenarios.
To monitor and understand AI solutions' performance, a range of metrics, including feedback from end users, should be employed.
Metrics relevant to the context and goals of AI solutions and business requirements should be selected.
Data validation should be performed periodically to check for inappropriate values, missing values, biasedness, training skew, or to detect drift.
Limitations, flaws, and potential issues should be properly addressed and communicated to stakeholders and users.
A rigorous testing procedure should be in place. Unit tests should test the components of individual solutions. Integration tests should test the seamless interaction between the components and quality. Statistical tests should check for data quality and drift.
Track and continuously monitor all deployed models. Compare and log model performance and update deployed model based on changing business requirements, data, and system performance.
Is Responsible AI Slowing Down Innovation?
Undoubtedly, adopting and implementing Responsible Artificial Intelligence can slow down the process, but we cannot say it is slowing down innovation. Using AI systems without Responsible, ethical, and human-centric approaches could cause a fast race that no longer belongs. If these systems start working in opposing human morals, ethics, and rights, people will no longer keep using them.
"I don't think we should spend time talking to people. They don't understand this technology. It can hinder progress."
Some people think Responsible AI takes a lot of time; it wastes time and hampers innovation. Hence, Leave things the way they are. However, responsible artificial intelligence is a new term, so it is required to give people the vision they need. It could be challenging to convince people and provide a picture, but it will deliver more innovative and robust systems later. We need to tell them taking things with care would take time. No doubt, creating relationships with partners and stakeholders takes time. It will result in a human-centric AI. Slowing innovation is committed to providing human-centric solutions that protect humans' fundamental rights and follow the rule of law. It will promote ethical deliberation, diversity, openness, and societal engagement.
Responsible AI Toolkit and Framework
There is no single tool or framework for implementing Responsible Artificial Intelligence, so let's discuss some of them that help embed key features of responsible AI in our system.
Tensorflow
What if tool: Check model performance for a range of parameters in the dataset and manipulate individual data points to check the output. It also allows data to be sorted with five buttons in different types of fairness based on mathematical measures. It just requires minimal coding to check the behaviour of trained models. It uses visuals to check performance across a wide range of parameters. It could integrate with Collaboratory, Jupyter, Cloud AI Notebooks, TensorBoard, and TFMA Fairness Indicators. It can support binary classification, multi-class classification, and regression of tabular, image, and text data.
LF AI
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AI Fairness 360 is an open-source, Responsible AI tool for fairness. It helps users understand, examine, and report various biases and mitigate them. It has ten bias mitigation algorithms and 70 fairness metrics.
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AI Explainability 360 is an open-source toolkit that provides model interpretability and explainability and helps users understand model decisions. It contains ten explainability algorithms and provides metrics of faithfulness and monotonicity as proxies for explainability.
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Adversarial Robustness Toolbox: It checks for adversarial threats in ML models to evaluate, defend, and verify against attacks. ART(Adversarial Robustness Toolbox) supports all famous ML frameworks and data. It comprises 39 attack modules, 29 defence modules, estimators, and metrics.
SHAP
SHAP stands for Shapley Additive Explanations, a game-theoretic approach that can explain the output of any ML model. It connects optimal credit allocation with local explanations using the classical Shapley values from game theory and their related extensions.
LIME
Lime stands for Local Interpretable Model-agnostic Explanations. It can reliably explain the predictions of text classifiers, classifiers on categorical data or NumPy arrays, and images. It gives a local linear approximation of the model's behaviour.
Counterfactuals
A counterfactual is a technique for explaining a prediction that describes the smallest change to the feature values that could change the prediction to a predefined output. For instance, the change in the value of a particular feature could change the state from rejected credit application to accepted.
Future of Responsible AI
People are looking for an approach that could be used to anticipate rather than react to risks. A standard process, communication, and transparency are required to achieve that. Therefore, demand for general and flexible responsible artificial intelligence is rising because its framework can handle different AI solutions, such as predicting credit risk or using video recommendation algorithms. The outcome that it will provide is understandable and readable for all types of people or stakeholders. So that respective audiences can use that outcome for their purpose. For instance, end-users may be meant to justify decisions and how they can report incorrect results.
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