Responsible AI/ML Alleviates Technological Risks and Security Concerns

by October 30, 2020 0 comments

Artificial Intelligence

As the usage of AI and ML increases, technology is becoming more pervasive.

Artificial Intelligence (AI) and Machine Learning (ML) is transforming industries and solving important and real-world challenges at a scale. The technology is maturing rapidly with seemingly limitless applications. These vast openings carry with it a deep responsibility to build AI that works for everyone.

AI applications have demonstrated its ability to automate daily works while also augmenting human capacity with new insight. However, with great power comes great responsibility. The fear of workforce displacement, loss of privacypotential biases in decision making and lack of control over automated systems and robots are some of the menacing possibilities. Artificial intelligence technologies in the commercial and public sector like autonomous cars, chatbots take over the tough human labor process by packing and endlessly answering human queries. However, the downside is that an autonomous car could cause an accident and a chatbot might learn to use offensive languages. These possible incidents have stoked fears of a ‘job apocalypse’ that concerns over inclusion, diversity, privacy and security.

As the usage of AI and ML increases, technology is becoming more pervasive. Technology is taking part in an increasing number of decisions like benefit payments, mortgage approvals, and medical diagnosis. When AI becomes a part of every working system, transparency and visibility disappears. One of the major threats that AI might imply is reinforcing existing human biases. These biases are unidentified and come about due to a lack of diverse perspective when developing and training the system.

In addressing all these issues and furthermore, Responsible AI/ML offers a way for everyone to adopt a ‘people first’ approach that is fair, accountable, honest, transparent and human-centric.


What is Responsible AI/ML?

Responsible AI/ML is the pursuit of bringing many of the critical concerns and practices together. The main focus of responsible AI is to ensure ethical, transparent and accountable use of AI technologies in a manner consistent with user expectations, organizational values, and societal laws and norms.

Responsible AI/ML guards against the use of biased data or algorithms, ensuring that the automated decisions are justified and explainable. It helps maintain user trust and individual privacy. By providing clear rules of engagement, responsible AI/ML allows organizations under public and congressional scrutiny to innovate and realize the transformative potential of AI that is both compelling and accountable.

Even though when Explainable AI (XAI) is used as a statistical method to explain machine learning models, the core matter is not just statistical questions. AI and ML should answer people’s questions through which a responsible AI/ML is formed. Responsible AI/ML tries to achieve maximum transparency and understanding of AI with the full view of models and their impacts. Responsible AI/ML comprises of six critical themes. They are Explainable AI, interpretable machine learning technology, ethical AI, secure AI, Human-centred AI and compliance.


Ways to mitigate risk with Responsible ML

report published by authors Patrick Hall, Navdeep Gill, and Ben Cox focuses on the technical issues of ML as well as human-centred issues such as security, fairness and privacy. The goal to promote these aspects is to erase the thin line between general practices in technology and Responsible AI. Some of the key content in the report are,

• People: Human in the loop- Why an organization’s machine learning culture is an important aspect of the responsible practice of ML.

• Processes: Taming the wild west of Machine Learning workflows- Suggestions for changing or updating your process to govern ML assets.

• Technology: Engineering ML for human trust and understanding- Tools that can help organizations build human trust and understanding into their ML systems.

Actionable Responsible ML Guidance- Core considerations for companies that want to drive value from ML.

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