CROSS-REFERENCE TO RELATED APPLICATIONS
[0001]
This application is a continuation-in-part of application Ser. No. 16/653,415, filed Oct. 15, 2019, and claims priority to EP Application No. 20208211.1, having a filing date of Nov. 17, 2020, the entire contents both of which are hereby incorporated by reference.
FIELD OF TECHNOLOGY
[0002]
The following relates to a computer-implemented method and a system for transforming a trained artificial intelligence model into a trustworthy artificial intelligence model.
BACKGROUND
[0003]
To facilitate a wide-spread acceptance of artificial intelligence (AI) systems guiding decision making in real-world applications, trustworthiness of deployed models is key. Not only in safety-critical applications such as autonomous driving or Computer-aided Diagnosis Systems (CDS), but also in dynamic open world systems in industry it is crucial for predictive models to be uncertainty-aware and yield well-calibrated—and thus trustworthy—predictions for both in-domain samples (“known unknowns”) as well as out-of-domain samples (“unknown unknowns”). In particular, in industrial and IoT settings deployed models may encounter erroneous and inconsistent inputs far away from the input domain throughout the life-cycle. In addition, the distribution of the input data may gradually move away from the distribution of the training data, e.g. due to wear and tear of the assets, maintenance procedures or change in usage patterns etc. The importance of technical robustness and safety in such settings is also highlighted by the recently published “Ethics guidelines for trustworthy AI” by the European Commission (https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai), requiring for trustworthy AI to be lawful, ethical and robust—technically and taking into account its social environment.
[0004]
In conventional approaches, for each new asset or new environment a new model is trained. However, this is costly, since production has to be stopped during the acquisition of new training data, labeling is expensive and also the procedure of training models comes at a high cost of human and IT resources.
[0005]
Moreover, statistical methods to detect domain drift based on the input data are known. These methods are highly specific to individual data sets. As known methods are not able to determine the effect of potential data drifts on the accuracy, a retraining of the model as well as a data generation process is necessary.
[0006]
Common approaches to account for predictive uncertainty include post-processing steps for trained neural networks (NN) and training probabilistic models, including Bayesian and non-Bayesian approaches. However, training such intrinsically uncertainty aware models from scratch comes at a high computational the cost. Moreover, highly specialized knowledge is needed to implement and train such models.
[0007]
However, while an increasing predictive entropy for an increasingly strong domain drift or perturbations can be an indicator for uncertainty-awareness, simply high predictive entropy is not sufficient for trustworthy predictions. For example, if the entropy is too high, the model will yield under-confident predictions and similarly, if the entropy is too low, predictions will be over-confident.
SUMMARY
[0008]
Considering the described drawbacks in the state-of-the-art, an aspect relates to provide a method and corresponding computer program product (non-transitory computer readable storage medium having instructions, which when executed by a processor, perform actions) and apparatus for providing a trustworthy artificial intelligence model.
[0009]
Embodiments of the invention relate to a computer-implemented method for transforming a trained artificial intelligence model into a trustworthy artificial intelligence model,
providing the trained artificial intelligence model via a user interface of a webservice platform, providing a validation data set, which is based on training data of the trained artificial intelligence model, generating generic samples by a computing component of the webservice platform based on the validation data set, transforming the trained artificial intelligence model by optimizing a calibration based on the generic samples.
[0014]
A conventional trained artificial intelligence (AI) model is provided as an input for the proposed method. Any trained AI model might be used and there is no specific requirement on the training level or on a maturity level or on the accuracy of the model. The higher the quality of the trained model, the easier and faster can the method be performed. Moreover the provided trustworthy artificial intelligence model has a corresponding better quality.
[0015]
As AI-models, for example AI based classifiers are used. For example, machine learning models might be used, e.g. deep learning-based models, neural networks, logistic regression models, random forest models, support vector machine models or tree-based models with decision trees as a basis might be used according to the application the AI-model is to be used for.
[0016]
Any set of training data, which has been used to train the model or which has been generated or were collected in order to train the model can be used to extract a validation data set. Thereby the validation data set can be the training data or a sub-set or a part of the training data or can be derived from the training data. The validation data set in particular comprises a set of labelled sample pairs, also referred to as samples.
[0017]
The transformation of the AI model is performed by a computing component of the web service platform. The input, i.e. the trained artificial intelligence model as well as a validation data set, is provided therefor to the computing component via a user interface of the web service platform. Such a user interface can be implemented by any applicable frontend, for example by a web app.
[0018]
The validation data set can be provided via the same user interface of the web service platform. Moreover, the training data set can be provided by the user interface and a validation data set is derived from the training data by the computing component.
[0019]
Based on the validation data set, generic samples are generated. Those generic samples reflect a domain drift, whereby, in an embodiment, a plurality of generic samples is generated, reflecting different levels or different degrees of domain drift. In other words, a plurality of generic samples is generated representing different strengths of perturbations. Those perturbations can reflect predictable or foreseeable or likely influences on the expected in-domain samples or they can alternatively reflect purely random modifications of the samples or they might further alternative reflect specific intended modifications in the sense of generation of adversarial samples. Thereby, a spectrum ranging from in-domain samples to out-of-domain samples is genera…
