[vc_row css=”.vc_custom_1667938523441{margin-top: -320px !important;margin-bottom: 387px !important;}” el_class=”vc_hidden-sm vc_hidden-xs”][vc_column][vc_row_inner css=”.vc_custom_1667928603492{margin-top: -360px !important;margin-bottom: 150px !important;}”][vc_column_inner][vc_column_text css=”.vc_custom_1667738431109{padding-top: -200px !important;}”]Quantum key distribution is a secure communication method that makes use of the properties of quantum mechanics to share a secret key between two parties. This key can then be used to encrypt and decrypt messages sent between the parties. Quantum communication is a field of physics that studies the transmission of information using quantum mechanical effects. Quantum entanglement is a phenomenon in which two particles become intimately linked, even if they are separated by large distances. This entanglement can be used to transmit information instantaneously between the particles, regardless of how far apart they are. Quantum cryptography is a method of securely transmitting information using the principles of quantum mechanics.[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column width=”3/4″][stm_post_details][vc_row_inner css=”.vc_custom_1625296361064{margin-right: 40px !important;background-color: #f2f4fa !important;}”][vc_column_inner width=”2/3″ css=”.vc_custom_1625296591748{margin-top: 30px !important;}”]
[/vc_column_inner][vc_column_inner width=”1/3″ css=”.vc_custom_1625441376387{margin-left: 40px !important;border-right-width: 10px !important;border-left-width: 10px !important;padding-top: 30px !important;padding-bottom: 30px !important;background: #153e4d url(https://equinoxgroup.eu/wp-content/uploads/2021/06/Website-Narration.jpg?id=4189) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_custom_heading icon=”fas fa-assistive-listening-systems” icon_size=”40″ stm_title_font_weight=”700″ text=”” font_container=”tag:h2|font_size:40|text_align:left|color:%23153e4d|line_height:0.8″ use_theme_fonts=”yes” css_animation=”none” subtitle=”Prefer to listen?” subtitle_color=”#ffffff”][vc_empty_space height=”20px”][vc_column_text]If you prefer to listen to, instead of reading the text on this page, all you need to do is to put your device sound on, hit the play button on the left, sit back, relax and leave everything else to us.
[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_empty_space][vc_custom_heading icon=”fas fa-balance-scale-left” icon_size=”40″ stm_title_font_weight=”700″ text=”Bias in Algorithms” font_container=”tag:h2|font_size:24|text_align:left” use_theme_fonts=”yes”][vc_column_text css=”.vc_custom_1667860103451{margin-top: 30px !important;}”]
Concerns about individual privacy are not the only risks organisations face when leveraging big data and machine learning to drive growth. Another is bias inherent in algorithms used to make decisions about individuals or groups of consumers. These biases often result from two factors: first, the fact that machine learning models are trained using historical data; second, human involvement in creating algorithms that may reflect personal prejudices. As a result, they may inadvertently discriminate against certain groups – such as women or minorities – or reinforce existing disparities based on income level, race, gender identity, geographic location, age group, religion, or sexual orientation.
A study by researchers at MIT, for instance, found that facial recognition technology sold by IBM was more accurate in identifying white men than darker-skinned women. In another case, Alphabet’s Google Street View cars were more likely photograph neighbourhoods populated mainly by people of colour than those mostly inhabited by white residents – an oversight resulting from the absence of detailed demographic information needed train computer vision system identify which areas map.
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