Many methods for automated software test generation, including some that explicitly use machine learning (and some that use ML more broadly conceived) derive new tests from existing tests (often referred to as seeds). Often, the seed tests from which new tests are derived are manually constructed, or at least simpler than the tests that are produced as the final outputs of such test generators. We propose annotation of generated tests with a provenance (trail) showing how individual generated tests of interest (especially failing tests) derive from seed tests, and how the population of generated tests relates to the original seed tests. In some cases, post-processing of generated tests can invalidate provenance information, in which case we also propose a method for attempting to construct "pseudo-provenance" describing how the tests could have been (partly) generated from seeds.
Demand is growing for more accountability in the technological systems that increasingly occupy our world. However, the complexity of many of these systems - often systems of systems - poses accountability challenges. This is because the details and nature of the data flows that interconnect and drive systems, which often occur across technical and organisational boundaries, tend to be opaque. This paper argues that data provenance methods show much promise as a technical means for increasing the transparency of these interconnected systems. Given concerns with the ever-increasing levels of automated and algorithmic decision-making, we make the case for decision provenance. This involves exposing the 'decision pipeline' by tracking the chain of inputs to, and flow-on effects from, the decisions and actions taken within these systems. This paper proposes decision provenance as a means to assist in raising levels of accountability, discusses relevant legal conceptions, and indicates some practical considerations for moving forward.
Research in AI within the Computer Science Department at the University of California, Los Angeles is loosely composed of three interacting and cooperating groups: (1) the Artificial Intelligence Laboratory, at 3677 Boelter Hall, which is concerned mainly with natural language processing and cognitive modelling, (2) the Cognitive Systems Laboratory, at 4731 Boelter Hall, which studies the nature of search, logic programming, heuristics, and formal methods, and (3) the Robotics and Vision Laboratory, at 3532 Boelter Hall, where research concentrates on robot control in manufacturing, pattern recognition, and expert systems for real-time processing.