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.

In association football, the development of multimodal summaries is of great importance to both broadcasters and spectators since a large number of viewers choose to follow just the soccer game highlights. The fundamental drive for the development of summarization systems is the requirement to manage huge amounts of data in different formats. By highlighting the most pertinent facts and limiting or omitting unnecessary aspects, summarization helps avoid "information overload." The properties of the audio signals during a particular event can be used to calculate excitement around that event and filter events based on their importance. A root-mean-square (RMS) analysis of audio events was carried out to analyse the excitement across the events in the SoccerNet dataset. It was clearly seen that important events with excitement have a high and distinguishable RMS audio intensity. It was also observed that the generated noise of the crowd was significantly different across various events and if it happened for the home or away team. The intensity was higher for events related to the home team. Likewise, as the wavelet has the benefit of integrating a wave with a specific period, Morlet wavelet analysis was performed for various event types, and the power of the signal across various wavelet scales was analyzed. A distinct signature across various wavelet scales was observed for different events.