THE GOAL OF THIS RESEARCH IS TO DEVELOP CONVERSATION ANALYSIS TO MEASURE AND MITIGATE INAPPROPRIATE TRUST IN VIRTUAL ASSISTANTS. THESE TRUST MEASUREMENTS WILL GUIDE SYSTEM DESIGN PARTICULARLY THE MULTIMODAL INTERACTIONS AND MODE SWITCHING AS WELL AS HOW TO MITIGATE OVER TRUST AND TRUST RECOVERY. WE WILL USE CONVERSATION ANALYSIS TO MEASURE TRUST AT MULTIPLE TIME-SCALES FROM REAL-TIME INTERACTIONS TO LONGITUDINAL MONITORING OF TRUST OVER A LONG DURATION EXPLORATION MISSION. CONVERSATION ANALYSIS PROVIDES A PROMISING BUT RELATIVELY UNEXPLORED APPROACH TO MEASURING TRUST. WE PROPOSE A CONVERSATION ANALYSIS AT THE MICRO MESO AND MACRO LEVELS WHICH INCLUDES NOT JUST THE WORDS BUT ALSO PAUSES AND FACIAL EXPRESSIONS. SPECIFICALLY AT THE MICRO-LEVEL CONVERSATION ELEMENTS INCLUDE VOICE INFLECTIONS PAUSES BETWEEN WORKS AND KEYSTROKES GAZE SHIFTS AND FACIAL EXPRESSIONS. THE MESO-LEVEL ANALYSIS INCLUDES WORDS EXCHANGED DURING INTERACTIONS WITH THE VIRTUAL ASSISTANT ALONG WITH OTHER TEAM INTERACTIONS AS THEY RELATE TO THE AUTOMATION. AT THE MACRO LEVEL CONVERSATIONAL ANALYSIS CONSIDERS INTERACTION TIME INTERACTION EFFORT FREQUENCY OF INTERACTION TURN-TAKING BARGING IN TENDENCY AND WHETHER IT IS THE PERSON OR THE VIRTUAL ASSISTANT WHO INITIATES THE INTERACTION. ADDITIONALLY PRIOR RESEARCH INTO CONVERSATIONAL ANALYSIS INDICATES THERE ARE NOVEL WAYS OF MANAGING OR CALIBRATING TRUST THROUGH THE PRESENTATION OF INFORMATION E.G. MANIPULATING THE TONE AND CADENCE OF THE SYSTEM WHEN USING SPEECH AND THROUGH FACIAL EXPRESSIONS (NASS&BRAVE 2005; DESTENO ET. AL. 2012). DUE TO TIME DELAYS IN COMMUNICATION LONG DURATION EXPLORATION MISSIONS WILL REQUIRE GREATER CREW AUTONOMY AND GREATER RELIANCE ON AUTOMATION. FOR THIS APPROACH TO WORK TRUST CALIBRATION NEEDS TO BE ENGINEERED INTO THE SYSTEM. TRUST IS A CRITICAL CONSTRUCT THAT MEDIATES HOW WELL HUMAN OPERATORS USE AUTOMATED SYSTEMS SUCH AS VIRTUAL ASSISTANTS THAT PROVIDE DECISION SUPPORT. TRUST AFFECTS PEOPLE.S WILLINGNESS TO RELY ON AUTOMATED SYSTEMS IN SITUATIONS THAT HAVE A DEGREE OF UNCERTAINTY AND RISK. TRUST STRONGLY AFFECTS THE EFFECTIVENESS OF HUMAN-AGENT COLLABORATION PARTICULARLY IN THE WILLINGNESS TO ACCEPT SUGGESTIONS FROM A VIRTUAL ASSISTANT. KNOWING WHETHER OR NOT TO TRUST AUTOMATION CAN BE FURTHER COMPLICATED BY LACK OF SLEEP WORKLOAD TASK RISK AND TASK COMPLEXITY. MOREOVER AS WE CONTINUE TO PUSH THE LIMITS OF INTELLIGENT SYSTEMS AND RELY ON THEM MORE AS DECISION AIDS TRUST CALIBRATION (I.E. OPERATOR TRUST IS AT A LEVEL WHICH MATCHES THE AUTOMATION'S CAPABILITIES) BECOMES ESSENTIAL TO MISSION EXECUTION. APPROPRIATE CALIBRATION OF TRUST REQUIRES MATCHING THE OPERATORS TRUST TO THE VIRTUAL ASSISTANT.S CURRENT CAPABILITIES. CALIBRATION OF TRUST IS NOT SOMETHING THAT CAN HAPPEN ONCE BUT MUST OCCUR THROUGHOUT THE LIFECYCLE OF THE INTERACTION BETWEEN OPERATOR AND AUTOMATED SYSTEM (HOFFMAN ET AL. 2009). TRUST IS A DYNAMIC CONSTRUCT THAT CONTINUOUSLY INCREASES AND DECREASES DUE TO A NUMBER OF FACTORS PRIMARY THE PERFORMANCE OF THE AUTOMATED SYSTEM I.E. HIGHER PERFORMANCE LEADS TO HIGHER TRUST AND VICE VERSA. ALTHOUGH MUCH EFFORT FOCUSSES ON CREATING MORE CAPABLE AND TRUSTWORTHY AUTOMATION LESS EFFORT HAS CONSIDERED THE EQUALLY IMPORTANT CONSIDERATION OF CREATING TRUSTABLE AUTOMATION. TRUSTABLE AUTOMATION IS AUTOMATION THAT IS UNDERSTANDABLE AND THAT NATURALLY PROMOTES CALIBRATED TRUST. THEREFORE WE AIM TO CREATE TRUSTABLE AUTOMATION BY CONTINUOUSLY MEASURING OPERATORS. TRUST UNOBTRUSIVELY AND IN REAL-TIME AND THEN USE THIS MEASURE TO GUIDE THE VIRTUAL AGENT TO EMPLOY ONE OR MORE COUNTERMEASURES TO CALIBRATE TRUST AND IMPROVE HUMAN-SYSTEM PERFORMANCE.