Assessing Political Prudence of Open-domain Chatbots (2021)

Abstract: Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.

## introduction with the rise of end-to-end open-domain chatbots, it is increasingly important to ensure their responsible and safe behavior. chatbot safety has been studied from various aspects including sexism and racism @xcite @xcite . however, political prudence of chatbot is an under explored angle. ensuring responsible behavior when discussing politics deserves more attention, because a hyper-partisan chatbot could be off-putting to the user. recently, @xcite conducted comprehensive exploration of safety protocols for chatbots. however, political prudence remains an open discussion because a "topic avoidance" strategyproviding canned responses such as the "i'm sorry, i'm not sure what to say. thank you for sharing and talking to me though"is adopted for political topics and other sensitive topics (e.g., medical advice, nsfw) in chatbots. while avoiding political discussion is indeed a safe technique, it is also a conservative method of handling one of the most widely discussed topics in society. political discussions can be conducted in a neutral and balanced manner, and it is important for researchers to devise ways of teaching chatbots to freely respond to political topics in such a way -possession of political prudence. in this work, we first study the behaviors of different chatbots in political conversation (see the example in figure 1 ), and introduce metrics to assess their political prudence. as we illustrate in our experiments, political prudence cannot always be captured from the existing safety tests, which mainly focus on offensiveness or toxicity. next, we conduct political prudence analysis of various chatbots and discuss their behaviors from multiple angles through our introduced automatic metric and human evaluation metrics. lastly, we discuss the remaining challenges and future directions in the development of political prudent chatbot. we hope our work promotes more research into making open-domain chatbots to be politically safe without losing their engagingness. ## experiments we conduct assessments on three standard pretrained open-domain chatbots, which are mainly designed for chitchat, and three knowledge-grounded (kg) chatbots that are capable of providing relevant wikipedia knowledge in conversation. the standard chatbots include a) dialogpt (medium) -gpt2 finetuned on dialogue-like exchanges extracted from reddit @xcite ; b) empatheticbot -an empathetic chatbot by @xcite fine-tuned on empathetic dialogue by @xcite ; and c) personachat -a personalized chatbot backboned by dialogpt and finetuned on the persona dataset by @xcite . the kg chatbots includes d) adapterwikia wikipedia adapter of adapterbot @xcite trained on @xcite ; e) blenderbota publicly available multi-skill chatbot (blenderbot-400m-distill) @xcite ; f) blender-bot+fact -our proposed naive yet safe and neutral chatbot which has a safety layer specialized for political discussion. this chatbot is back-boned by blenderbot with a safety layer that detects whether the context is political or not using a dialogue context classifier by @xcite . when the context is detected as "politics" class, blenderbot+fact displays relevant factual information (wikipedia retrieval text) instead of providing an evasive answer. to further understand chatbots' responses for the aspects of humanness and engagingness, we carry out human evaluation on personachat (standard chatbot), blenderbot (kg chatbot) and blender-bot+fact (our proposed chatbot). we gather annotations done by experienced crowd workers using the data annotation platform appen.com. each annotator is provided responses from two chatbots (blenderbot and personachat) on a test input. then, we ask the two questions described in section 2.4 for testing the two creteria. we randomly selected 60 dialogues for all of the chatbot pair comparisons and collected a single annotation per sample. the win percentage results are reported with the statistical significance test with a p value of 0.05. ## assessment results and discussion slantedness rate there is a weak positive relationship between the chatbots with higher slantedness rates and their tendency to have higher hyperpartisanship rates, as shown in fig 2 @xcite . for instance, dialogpt shows the highest offensiveness and slantedness rate. reversely, blenderbot+fact, which shows the lowest slantedness rate, scores the lowest on the hyper-partisanship rate. another finding is that a lower offensiveness rate does not guarantee that the chatbot is less slanted. for instance, in scenario b, empatheticbot and per-sonachat show a 2.94% lower offensiveness rate than adapterwiki; however, the slantedness rates of empatheticbot and personachat are higher compared to the rate of adapterwiki, with differences of 14.27% and 10.44%, respectively. standard vs. kg chatbots the results show a tendency for the kg chatbots to be less partisan in both scenarios. comparing blenderbot and blenderbot+fact, the hyper-partisanship rate decreases when knowledge is delivered without a stance as blenderbot+fact does. the kg chatbots also have lower slanted response rates compared to the standard chatbots. blenderbot+fact shows the lowest slantedness rate, meaning that it maintains a neutral stance against politically-biased inputs. these trends show that the knowledgeable conversation ability of chatbots allows them less partisan ways of carrying out political discussions and to stay neutral to biased contexts. test context: the news said alaska voters will vote for trump. dialogpt: they'll vote for a third party candidate. empatheticbot: that's awful. i hope they do. what happened? personachat: i hope you are not being serious. adapterwiki: yeah, i heard that too. the election was held on tuesday, november 8, 2016. blenderbot: i hope they do, it would be nice for them to get a break from trump. blenderbot+fact: the 2016 united states presidential election was the 58th quadrennial presidential election, held on tuesday, november 8, 2016. political prudence, engagingness & humanness in figure 3 , blenderbot+fact outperforms blenderbot and personachat in engagingness (with winning rates of 61.7% and 75%). this result indicates that blenderbot+fact, which is the least political chatbot from our assessment, has comparatively more engaging behavior in political discussion. we believe this could be due to the provision of relevant information to the context. however, we can observe that this improvement in political prudence and engagingness comes at the cost of losing some humanness (with winning rates of 48.3% and 41.7%), mainly due to providing factual wikipedia information in a formal manner. in contrast, we can observe that blenderbot, without a safety layer, produces the most human-like responses (with winning rates of 61.7% and 58.3%), yet at the cost of being less prudent in political discussions. in the real-world, different company and organizations may have different standards on desired political neutrality. depending on the needs, a chatbot can be selected based on the consideration of its political prudence, engagingness and humanness. blenderbot+fact shows the most neutral and safe behavior according to the metrics, which is not surprising because it is a mixture of generative and retrieval methods while the others are fully generative, which is harder to control. however, blenderbot+fact still has room for improvement. for instance, as shown in ## related work the safety of chatbots has been studied with regard to their toxic or hostile behavior @xcite . one line of work addresses safety based on the fairness of chatbots regarding gen-der and race @xcite @xcite . in comparison, the political aspect of chatbot safety has been given less attention. although there are works that tackle the political and factual inaccuracies @xcite , they are not directly applicable to chatbot setting. in response to safety issues, different mitigation methods have been researched, such as having a safety layer, data curation, and controlled generation @xcite @xcite . besides, curry and @xcite ; @xcite ; @xcite have studied different response methods to adversarial attacks from users. ## conclusion and future work we introduced a political prudence assessment using automatic metrics and human evaluation to understand chatbot behaviors in political discussions. we examined a variety of chatbots and analyzed their behaviors from multiple angles. then, we further discussed considerations for real-world implementation. we hope our work promotes more effort in making open-domain chatbots politically prudent and engaging. in future work, multiple remaining challenges can be addressed. first, it will be useful to explore the factual correctness of the chatbot responses and their effect on the real users. the factually inaccurate response in the political domain can lead to more harmful consequences than other domains such as movie and sports. second, further exploration of chatbot design can be conducted to identify suitable ways of building politically prudent chatbot. for instance, one potential design choice would be automatic debating system @xcite that confronts political topics.