Page 39 - nai udaan march 2024

Student’s Innovation

BiasEncoder

Promoting Responsible AI For All

Problem Statement

In today's AI-driven world, ensuring fair and responsible

technology is critical. According to a McKinsey report, AI
biases could result in global financial losses of up to $5.2
trillion by 2025 and can have a significant impact on areas
like education, healthcare and justice leading to further soci-
etal harm and inequality. Detection of Biases in AI programs
is a very difficult task and takes a lot of time of developers to
identify potential biases in the code.

Solution I have Developed

BiasEncoder, is comprehensively, identifying subtle biases
an AI-powered that may otherwise go unnoticed.
platform that By decoding the content of uploaded code,
can detect and BiasEncoder can pinpoint the specific types of
mitigate biases biases present and provide actionable recom-
in AI programs, mendations for mitigation. This process
enabling devel- empowers developers and tech firms to recti-
opers and tech fy biases efficiently, fostering the develop-
firms to create ment of AI systems that are equitable and
bias-free programs. inclusive.
BiasEncoder uses Recurrent Neural Net- One of the key strengths of BiasEncoder lies
works, Random Forest Classifiers, and sever- in its versatility and adaptability. By utilizing
al other algorithms to analyze biases in AI Intel's OpenVino Toolkit, BiasEncoder not
programs. We also use Intel's OpenVino Tool- only delivers enhanced runtime performance
kit for faster runtime and enhanced accuracy. but also ensures unparalleled accuracy in bias
detection. This synergy between cutting-edge
BiasEncoder offers a comprehensive solution technologies enables BiasEncoder to scale
that leverages cutting-edge machine learning seamlessly, catering to the evolving needs of a
algorithms to detect and mitigate biases in AI diverse range of industries and applications.
programs. At the heart of BiasEncoder lies a
sophisticated infrastructure that combines the Whether it's optimizing healthcare algo-
power of Recurrent Neural Networks rithms to mitigate biases in diagnosis or
(RNNs), Random Forest Classifiers, and other enhancing fairness in hiring practices,
advanced algorithms. This robust framework BiasEncoder serves as a vital tool for promot-
enables BiasEncoder to analyse AI programs ing equity and social justice.

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