Human-Centered Metrics for Dialog System Evaluation

We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens: conversational agents express a diversity of both states (short-term factors like emotions) and traits (longer-term factors like personality) just as people do. These interpretable metrics consist of five measures from established psychology constructs that can be applied both across dialogs and on turns within dialogs: emotional entropy, linguistic style and emotion matching, as well as agreeableness and empathy. We compare these human metrics against 6 state-of-the-art automatic metrics (e.g. BARTScore and BLEURT) on 7 standard dialog system data sets. We also introduce a novel data set, the Three Bot Dialog Evaluation Corpus, which consists of annotated conversations from ChatGPT, GPT-3, and BlenderBot. We demonstrate the proposed human metrics offer novel information, are uncorrelated with automatic metrics, and lead to increased accuracy beyond existing automatic metrics for predicting crowd-sourced dialog judgements. The interpretability and unique signal of our proposed human-centered framework make it a valuable tool for evaluating and improving dialog systems.

Robust language-based mental health assessments in time and space through social media

Compared to physical health, population mental health measurement in the U.S. is very coarse-grained. Currently, in the largest population surveys, such as those carried out by the Centers for Disease Control or Gallup, mental health is only broadly captured through "mentally unhealthy days" or "sadness", and limited to relatively infrequent state or metropolitan estimates. Through the large scale analysis of social media data, robust estimation of population mental health is feasible at much higher resolutions, up to weekly estimates for counties. In the present work, we validate a pipeline that uses a sample of 1.2 billion Tweets from 2 million geo-located users to estimate mental health changes for the two leading mental health conditions, depression and anxiety. We find moderate to large associations between the language-based mental health assessments and survey scores from Gallup for multiple levels of granularity, down to the county-week (fixed effects $\beta = .25$ to $1.58$; $p<.001$). Language-based assessment allows for the cost-effective and scalable monitoring of population mental health at weekly time scales. Such spatially fine-grained time series are well suited to monitor effects of societal events and policies as well as enable quasi-experimental study designs in population health and other disciplines. Beyond mental health in the U.S., this method generalizes to a broad set of psychological outcomes and allows for community measurement in under-resourced settings where no traditional survey measures - but social media data - are available.

Ares: A System-Oriented Wargame Framework for Adversarial ML

Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved into an eternal war between defenders, who seek to increase the robustness of ML models against adversarial attacks, and adversaries, who seek to develop better attacks capable of weakening or defeating these defenses. This domain, however, has found little buy-in from ML practitioners, who are neither overtly concerned about these attacks affecting their systems in the real world nor are willing to trade off the accuracy of their models in pursuit of robustness against these attacks. In this paper, we motivate the design and implementation of Ares, an evaluation framework for adversarial ML that allows researchers to explore attacks and defenses in a realistic wargame-like environment. Ares frames the conflict between the attacker and defender as two agents in a reinforcement learning environment with opposing objectives. This allows the introduction of system-level evaluation metrics such as time to failure and evaluation of complex strategies such as moving target defenses. We provide the results of our initial exploration involving a white-box attacker against an adversarially trained defender.