Firms need to understand how Machine Learning (ML) models function in order to trust them. Increasing model transparency will not only grow the rewards but also multiply the risks.
As firms move towards the adoption of machine learning, Artificial Intelligence (AI) is generating substantial security risks.
One of the most significant risks associated with AI remains the ML-based models operating as “black boxes.” The deep learning models composed of artificial neural networks have complicated the process of deriving automated inferences. These complications increase the risks associated with AI models. ML-based applications may inadvertently get influenced by biases and other adverse factors while producing automated decisions. To mitigate the risks, firms are starting to demand enhanced transparency into how ML operates, focusing on the entire workflow in which models are trained, built, and deployed.
There are many frameworks for maintaining the algorithmic transparency of AI models to ensure explainability, interpretability, and accountability. Business demands flexibility, but IT needs control. This has pushed the need of firms to rely on different frameworks to secure algorithm transparency. All these tools and techniques assist the data scientists in generating explanations to understand which data inputs drove different algorithmic inferences under various circumstances. However, sadly, these frameworks can be easily hacked, thereby reducing trust in the explanations they generate and exposing the risks they create:
Algorithmic deceptions may sneak into the public record – Dishonest parties may hack the narrative explanations generated by these algorithms to obscure or misrepresent any biases. In other words, “perturbation-based” approaches can be tricked into creating “safe” reasons for algorithmic behaviors that are definitely biased.
Technical vulnerabilities may get disclosed accidentally – Revealing information about machine learning algorithms can make them highly vulnerable to attacks. Complete transparency into how machine learning models function will expose them to attacks designed either to trick the inferences from live operational data or by injecting bogus data into their training workflows.
Intellectual property theft may be encouraged – Entire ML algorithms and training data sets can get stolen through their APIs and other features. Transparency regarding how ML models operate may enable the underlying models to be reconstructed with full reliability. Similarly, transparency will also make it possible to partially or entirely reconstruct training data sets, which is an attack known as “model inversion.”
Privacy violations may run rampant. ML transparency may make it possible for unauthorized third parties to ascertain a particular individual’s data record through a “membership inference attack,” to enable hackers to unlock considerable amounts of privacy-sensitive data.
To mitigate such technical risks of algorithmic transparency, enterprise data professionals need to adhere to the below strategies:
- Firms should have control access to model outputs and monitor to prevent data abuse.
- Add controlled amounts of “perturbations” into the data used to train transparent ML models to make it difficult for adversarial hackers to use model manipulations to gain insight into the original raw data itself.
- Insert intermediary layers between the final transparent ML models and the raw data, making it difficult for an unauthorized third party to recover the full training data from the explanations generated against final models.
- In addition to these risks of a technical nature, enterprises get exposed to more lawsuits and regulatory scrutiny.
Without sacrificing ML transparency, firms need to have a clear objective of mitigating these broader business risks .
Enterprises will need to monitor these explanations for irregularities continually, to derive evidence that they or the models have been hacked. This is a critical concern because trust in the AI technology will come tumbling down if the enterprises that build and train ML models can’t vouch for the transparency of the models’ official documentation.