@article {43079,
	title = {State of Gender Equality in and by Artificial Intelligence},
	journal = {IADIS International Journal on Computer Science \& Information Systems},
	volume = {17},
	number = {2},
	year = {2022},
	month = {12/2022},
	pages = {31-48},
	publisher = {IADIS},
	abstract = {When talking about sustainability, we usually think that it is only about safeguarding the environment; nothing is further from reality. Of course, the environment is a crucial component of sustainability and our survival, but it is important to recall that the society and the economy play important roles in this regard, and without the interconnection and development of these three perspectives it will not be possible to achieve sustainable progress. The Sustainable Development Goals (SDGs) established by the United Nations (UN) defend this idea and address the main challenges that humanity faces. One of these challenges is gender equality, which is identified in the perspective of social sustainability through SDG 5. Gender equality is a very complex and difficult challenge to address due to the great cultural diversity of our society. Thus, achieving this goal will require laying a solid foundation and working together by combining very different fields of knowledge. In this sense, Artificial Intelligence (AI) is one of the fields that is currently having the greatest impact and relevance for the development of new technologies and for the advancement of numerous areas. This growing evolution of AI demonstrates that its repercussions at the social level must be analyzed and addressed in such a way that AI becomes a positive asset for sustainability and, in this particular case, for gender equality.For all these reasons, this study aims to analyze the current state of the art and collect the existing knowledge in the fields of AI and gender equality, by conducting a Systematic Mapping Study (SMS). The obtained results and findings have allowed us to identify the most relevant advances in this regard, as well as the gaps and drawbacks that currently exist and on which we must urgently focus to address gender equality both in and by AI. In the same way, these findings demonstrate the limited joint development of both fields, but also indicate an increase in the relevance and the number of proposals that these fields are receiving in recent years.},
	keywords = {artificial intelligence, Gender Equality, Literature Analysis, Literature Review, Social Sustainability},
	issn = {1646-3692},
	url = {https://www.iadisportal.org/ijcsis/papers/2022170203.pdf},
	author = {Jose David {Pat{\'o}n-Romero} and Vinuesa, Ricardo and Jaccheri, Letizia and Maria Teresa {Baldassarre}}
}
@inproceedings {Simula.SE.385,
	title = {A Preliminary Study of Sequence Effects in Judgment-Based Software Development Work- Effort Estimation},
	journal = {Proceedings, 12th International Conference on Evaluation and Assessment in Software Engineering},
	year = {2008},
	publisher = {University of Bari, Italy},
	type = {Conference},
	abstract = {Context: Software development effort estimates are often inaccurate, and this inaccuracy cause problems for the clients as well as the providers. Consequently, we need more knowledge about the estimation processes, so that we can improve them. Objective: This study investigates how initial judgment-based estimation of work effort in software development affects subsequent, unrelated estimation work. Method: Fifty-six software professionals from the same company were allocated randomly to two groups. One group estimated the most likely effort required to complete a small software development task, while the other group estimated the effort required to complete a large task. After that, all the subjects estimated the effort required to complete the same medium-sized task. We replicated the experiment in another company (with 17 software professionals). Results: We found that sequence effects may have a strong impact on judgment-based effort estimates. Both in the first experiment and in the replication, the subsequent estimates were assimilated towards the subjects{\textquoteright} initial estimate, i.e., the group that began with a small task supplied, on average, lower estimates of the medium-sized task than the group that began with the large task. Conclusion: The results of this study suggest that knowledge about sequence effects may be important in order to improve estimation processes. However, currently we have a quite incomplete understanding of how, when and how much sequence effects affect effort estimation. Consequently, further research is needed.},
	author = {Grimstad, Stein and J{\o}rgensen, Magne},
	editor = {Visaggio, Guiseppe and Maria Teresa {Baldassarre} and Linkman, Stephen and Turner, Mark}
}
