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How Bug Bounty Programs Can Help Develop Robust AI-Based Solutions

How Bug Bounty Programs Can Help Develop Robust AI-Based Solutions

Bug bounty programs

AI-based companies are heavily relying on the black box — neural networks — without being critical about the results. However, stakeholders are concerned about the absence of transparency and expect organizations to communicate the factors that are significant for the results their AI-based products deliver.

This has put AI-based companies in a fix as they fail to bring transparency among users and clients. Although they can leverage various solutions that claim to pinpoint the variables which have the most impact on the outputs, such solutions do not cater to the diverse needs of organizations while developing products. Consequently, firms should adopt methodologies similar to the bug bounty program to bring trust and transparency among stakeholders.

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Bug bounty programs are actively used by many organizations who like to determine vulnerabilities and fix them for delivering robust products to the market. This not only allows companies to evaluate their capabilities but also increases their brand value.

Here are the advantages that AI-based companies can gain if they implement the bug bounty program:-

Explainability

A bug bounty program can be helpful for companies when it comes to evaluating their outputs based on the explainability. Often AI agents are biased. Thus, organizations that buy your products need to trust the results it provides. Firms do not prefer to make business decisions using AI-solutions that fail to explain the factors that lead to the outcomes.

With numerous examples of bias in AI-solutions – especially in facial recognition – companies should be critical of how transparent the solutions are. Consequently, by allowing researchers from diverse backgrounds to question the results of agents, firms can drastically reduce their chances of providing any bias or wrong output with their models.

Adversarial Attacks

As researchers have failed to integrate common sense in AI agents, models are prone to adversarial attacks. Thus, providing the opportunity for researchers to identify the sensitivity of agents’ unforeseen adversaries can help companies enhance their products and ensure the reliability of outputs even in a relatively new environment.

Undoubtedly, one cannot eliminate the adversarial attacks completely but can be more aware of situations when the agents behave abruptly. You can train models to avoid making wrong decisions in some of the prevailing circumstances. This bounty here can be dependent on how common a situation is, and how often that can happen with the agent. Something totally out of the box will always confuse them. Therefore, the idea is to award only the ones based on the repeatability of the possible adversaries. 

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Brand Value

Companies that indulge in bug bounty programs gain a significant surge in brand value as it demonstrates how confident they are. Some of the biggest brands, like Apple, Microsoft, Google, Facebook, and OYO have been throwing such challenges for years. However, these are mostly related to cybersecurity, and not for AI-products. 

A bug bounty program can help your products stand out, and can work as a marketing strategy too. If companies are optimistic about their products, such programs can help them stand out in the market, thereby increasing sales.   

Cybersecurity

Cybersecurity has always been an essential part of any product, but with the introduction of various data protection laws, organizations cannot take any risks. Even a slightest oversight can lead to not just financial loss but also lead to decline in credibility among users. Thus, vulnerability management has become essential for firms due to the rise of an increasing number of data-driven IoT devices.

Bug bounty programs not only assist you in finding loopholes but also help you in evaluating the in-house cybersecurity department. Therefore, cybersecurity remains an essential factor for the bug bounty program, along with adversarial and explainability.

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