Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing (2021)

Abstract: The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers.

## revisiting the ethics of crowdsourcing the information age brought with it the internet, big data, smartphones, ai, and along with these, a plethora of complex ethical challenges. as a result, there is growing concern and discussion on ethics within the research community at large, including the nlp community. this is manifested in new ethics-focused workshops, ethics conference panels and relevant updates to peer review forms. while ethics in nlp has multiple aspects, most recent attention focuses on pressing issues related to the societal impact of nlp. these include discrimination, exclusion, over-generalization, bias, and fairness @xcite . other works are concerned with the ethical implications of nlp shared tasks @xcite , and introducing ethics into the nlp curriculum @xcite . a substantial amount of nlp research now takes advantage of crowdworkers -workers on crowdsourcing platforms such as amazon mechanical turk (known also as amt or mturk), figure eight foot_0 , appen, upwork, prolific, hybrid, tencent questionnaire, and baidu zhongbao, as well as internal crowdsourcing platforms in companies such as microsoft and apple. workers are recruited to label, evaluate, and produce data. in the pre-internet era, such tasks (e.g. part-of-speech (pos) tagging) were done by hiring expert annotators or linguistics students. however, these are now mostly replaced by crowdworkers due to lower costs, convenience, speed, and scalability. overall, the general consensus in the literature is that as long as the pay to the crowdworkers is "fair" (minimum hourly wage or above), there are no further ethical concerns, and there is no need for approval by an institutional review board 2 (with some exceptions). for example, @xcite mention that "[w]ork on existing corpora is unlikely to raise any flags that would require an irb approval", with a footnote that there are "a few exceptions". @xcite mention that only " @xcite small number of universities have insisted on institutional review board approval for mturk experiments". as another example, nlp students are being taught that "paid labeling does not require irb approval" since " @xcite t's not an experiment accepted papers papers using payment irb year acl emnlp naacl all crowdsourcing mentioned mentioned 2015 318 312 186 816 59 (7%) 4 (7%) 0 2016 328 264 182 774 82 (11%) 15 (18%) 0 2017 302 323 -625 57 (9%) 12 (21%) 3 2018 381 549 332 1262 136 (11%) 17 (13%) 1 2019 660 683 423 1766 189 (11%) 32 (17%) 5 2020 779 754 -1533 180 (12%) 42 (23%) 5 total 2768 2885 1123 6776 703 (10%) 122 (17%) 14 with human subjects" (carnegie-mellon university, language technologies institute, 2020). indeed, nlp papers that involve crowdsourced work rarely mention a review by an ethics board. in this work, we wish to revisit the ethical issues of crowdsourcing in the nlp context, highlight several issues of concern, and suggest ways forward. from our survey of top nlp conferences, we find that crowdsourcing tasks are growing rapidly within the community. we therefore establish a common understanding of research ethics and how it relates to crowdsourcing. we demonstrate that the existing ethical framework is often inadequate and does not seek to protect crowdsourced workers. we then dispel common misunderstandings regarding the irb process that nlp researchers might harbor. and finally, we outline how to apply ethical values as guidelines to minimize potential harms, and conclude with recommendations. ## the rise and rise of nlp crowdsourcing to get a sense of the extent and growth of crowdsourced tasks within the nlp research community, we analyzed the proceedings of three top nlp conferences: acl, emnlp, and naacl (also known as naacl-hlt) 3 . we scanned the annual proceedings of these conferences in the six years from 2015 to 2020, looking for papers that mention direct employment of crowdsourced workers. all together, ## acl is the annual meeting of the association for computational linguistics, emnlp is the conference on empirical methods in natural language processing, and naacl is the annual conference of the north american chapter of the association for computational linguistics: human language technologies. renumeration for each paper that uses crowdsourced labor, we checked whether the authors discuss payment or labor-related issues. out of the 703 papers, 122 (17%) discuss payment, either by detailing the amount paid per task, the worker's hourly wages, or declaring that wages were paid ethically. while in some cases researchers emphasize fair payment (e.g., @xcite ensured "a pay rate of at least $15/hour"), many other papers are more concerned about the cost of dataset acquisition, and thus mention the cost per task or the total dataset cost, but not the hourly compensation. irb review finally, we also checked whether authors mention a review by an irb (or equivalent body) for their research. we found very few papers that mention an irb approval or exemption -a total of 14 papers -which make up only 2% of the works that use crowdsourcing. ## the rules and institutions of research ethics given the increasing use of crowdsourced nlp tasks, how can researchers ensure ethical concerns are reasonably addressed? should a researcher make a judgement call and decide which tasks pose a risk of harm to the worker, and which are benign? to answer such questions, we will first explore the existing ethical framework used by researchers in the biomedical, social, and behavioral sciences. ## dispelling irb misconceptions as we now see, if workers constitute human subjects, an irb application is required. to clarify any other misconceptions that might be present within the community, we list key points related to the irb process and dispel misconceptions around them. while not exhaustive, the list can serve both researchers and reviewers. ## ways forward the use of crowdworkers is growing within the nlp community, but the ethical framework set in place to guarantee their ethical treatment (whether de jure or de facto) did not anticipate the emergence of crowdsourcing platforms. in most crowdsourced nlp tasks, researchers do not intend to gather information about the worker. however, the crowdsourcing platform often autonomously collects such information. as a result, it is often difficult to determine whether crowdworkers constitute human subjects -which hinges on whether the researcher collects information about the worker. however, a determination that all crowdworkers are human subjects -and thus mandate an irb approval for government-supported institutions -might create a "chilling effect" and disadvantage university researchers compared to industry-affiliated researchers. the effect is exacerbated in institutions where the ethics committee is heavily bureaucratic and does not offer a streamlined, expedited exemption process for low-to-no risk studies. whether their crowdsourced task requires irb application or not, we recommend that the ethicsaware researcher should carefully examine their study in light of the three principles set up by the belmont report: respect for persons, beneficence, and justice. and while this mandates fair pay, it is important to note that this is just one of the implications. there are other ethical considerations that are often overlooked -in particular risk assessment of causing psychological harm and exposure of sensitive information. thus, we recommend increasing awareness of the potential ethical implications of crowdsourced nlp tasks. as nlp researchers are now encouraged to add an "ethical considerations" section to their papers (naacl, 2020), they should also be encouraged to carefully weigh potential benefits against risks related to the crowdsourced task. we also propose increasing awareness by disseminating relevant knowledge and information. an educational ethics resource created using a community effort could serve as a beneficial first step. such a resource can include guidelines, checklists, and case studies that are specific to the ethical challenges of crowdsourced tasks in the context of nlp research. we believe that the creation of such a resource can serve as a springboard for a necessary nuanced conversation regarding the ethical use of crowdworkers in the nlp community.