8 research outputs found
Anticipatory Thinking Challenges in Open Worlds: Risk Management
Anticipatory thinking drives our ability to manage risk - identification and
mitigation - in everyday life, from bringing an umbrella when it might rain to
buying car insurance. As AI systems become part of everyday life, they too have
begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and
Go agents have similar capabilities to humans, implicitly managing risks
presented by their opponents. To further increase performance in these tasks,
out-of-distribution evaluation can characterize a model's bias, what we view as
a type of risk management. However, learning to identify and mitigate
low-frequency, high-impact risks is at odds with the observational bias
required to train machine learning models. StarCraft and Go are closed-world
domains whose risks are known and mitigations well documented, ideal for
learning through repetition. Adversarial filtering datasets provide difficult
examples but are laborious to curate and static, both barriers to real-world
risk management. Adversarial robustness focuses on model poisoning under the
assumption there is an adversary with malicious intent, without considering
naturally occurring adversarial examples. These methods are all important steps
towards improving risk management but do so without considering open-worlds. We
unify these open-world risk management challenges with two contributions. The
first is our perception challenges, designed for agents with imperfect
perceptions of their environment whose consequences have a high impact. Our
second contribution are cognition challenges, designed for agents that must
dynamically adjust their risk exposure as they identify new risks and learn new
mitigations. Our goal with these challenges is to spur research into solutions
that assess and improve the anticipatory thinking required by AI agents to
manage risk in open-worlds and ultimately the real-world.Comment: 4 pages, 3 figures, appeared in the non-archival AAAI 2022 Spring
Syposium on "Designing Artificial Intelligence for Open Worlds
Towards a fuller understanding of neurons with Clustered Compositional Explanations
Compositional Explanations is a method for identifying logical formulas of
concepts that approximate the neurons' behavior. However, these explanations
are linked to the small spectrum of neuron activations (i.e., the highest ones)
used to check the alignment, thus lacking completeness. In this paper, we
propose a generalization, called Clustered Compositional Explanations, that
combines Compositional Explanations with clustering and a novel search
heuristic to approximate a broader spectrum of the neurons' behavior. We define
and address the problems connected to the application of these methods to
multiple ranges of activations, analyze the insights retrievable by using our
algorithm, and propose desiderata qualities that can be used to study the
explanations returned by different algorithms.Comment: Accepted at NeurIPS 202
Complaint-driven Training Data Debugging for Query 2.0
As the need for machine learning (ML) increases rapidly across all industry
sectors, there is a significant interest among commercial database providers to
support "Query 2.0", which integrates model inference into SQL queries.
Debugging Query 2.0 is very challenging since an unexpected query result may be
caused by the bugs in training data (e.g., wrong labels, corrupted features).
In response, we propose Rain, a complaint-driven training data debugging
system. Rain allows users to specify complaints over the query's intermediate
or final output, and aims to return a minimum set of training examples so that
if they were removed, the complaints would be resolved. To the best of our
knowledge, we are the first to study this problem. A naive solution requires
retraining an exponential number of ML models. We propose two novel heuristic
approaches based on influence functions which both require linear retraining
steps. We provide an in-depth analytical and empirical analysis of the two
approaches and conduct extensive experiments to evaluate their effectiveness
using four real-world datasets. Results show that Rain achieves the highest
recall@k among all the baselines while still returns results interactively.Comment: Proceedings of the 2020 ACM SIGMOD International Conference on
Management of Dat
Accountability Layers: Explaining Complex System Failures by Parts
With the rise of AI used for critical decision-making, many important predictions are made by complex and opaque AI algorithms. The aim of eXplainable Artificial Intelligence (XAI) is to make these opaque decision-making algorithms more transparent and trustworthy. This is often done by constructing an ``explainable model'' for a single modality or subsystem. However, this approach fails for complex systems that are made out of multiple parts. In this paper, I discuss how to explain complex system failures. I represent a complex machine as a hierarchical model of introspective sub-systems working together towards a common goal. The subsystems communicate in a common symbolic language. This work creates a set of explanatory accountability layers for trustworthy AI
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI
There is broad agreement that Artificial Intelligence (AI) systems,
particularly those using Machine Learning (ML), should be able to "explain"
their behavior. Unfortunately, there is little agreement as to what constitutes
an "explanation." This has caused a disconnect between the explanations that
systems produce in service of explainable Artificial Intelligence (XAI) and
those explanations that users and other audiences actually need, which should
be defined by the full spectrum of functional roles, audiences, and
capabilities for explanation. In this paper, we explore the features of
explanations and how to use those features in evaluating their utility. We
focus on the requirements for explanations defined by their functional role,
the knowledge states of users who are trying to understand them, and the
availability of the information needed to generate them. Further, we discuss
the risk of XAI enabling trust in systems without establishing their
trustworthiness and define a critical next step for the field of XAI to
establish metrics to guide and ground the utility of system-generated
explanations
Reports of the AAAI 2019 Spring Symposium Series
Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must build machines that make sense of what goes on in their environment, a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates